





'''simple docstring'''

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch




def    _a    (							lowerCAmelCase_	):  # picklable for multiprocessing





      """simple docstring"""



      return x.sum()




def    _a    (							lowerCAmelCase_	):  # picklable for multiprocessing





      """simple docstring"""



      return i + 1





@dataclass
class lowerCamelCase						:
  _lowercase  :	int
  _lowercase  :	str





class lowerCamelCase						(a__					):







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     {}
        _snake_case		:	Union[str, Any]					     =     []
        _snake_case		:	str					     =     1
        _snake_case		:	Dict					     =     [1, 2]
        _snake_case		:	Tuple					     =     {'''a''': 1, '''b''': 2}
        _snake_case		:	str					     =     {'''a''': [1, 2], '''b''': [3, 4]}
        _snake_case		:	List[Any]					     =     {'''a''': {'''1''': 1}, '''b''': 2}
        _snake_case		:	str					     =     {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        _snake_case		:	Dict					     =     {}
        _snake_case		:	Union[str, Any]					     =     []
        _snake_case		:	Tuple					     =     2
        _snake_case		:	List[Any]					     =     [2, 3]
        _snake_case		:	List[Any]					     =     {'''a''': 2, '''b''': 3}
        _snake_case		:	Union[str, Any]					     =     {'''a''': [2, 3], '''b''': [4, 5]}
        _snake_case		:	List[Any]					     =     {'''a''': {'''1''': 2}, '''b''': 3}
        _snake_case		:	Tuple					     =     {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__		)     ,     lowercase__		)

        _snake_case		:	str					     =     2
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)

        _snake_case		:	List[str]					     =     {'''a''': np.eye(2		), '''b''': np.zeros(3		), '''c''': np.ones(2		)}
        _snake_case		:	Any					     =     {'''a''': 2, '''b''': 0, '''c''': 2}
        _snake_case		:	Any					     =     {
            '''a''': np.eye(2		).astype(lowercase__		),
            '''b''': np.zeros(3		).astype(lowercase__		),
            '''c''': np.ones(2		).astype(lowercase__		),
        }
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     map_numpy=lowercase__		)     ,     lowercase__		)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(lowercase__     ,     lowercase__     ,     map_numpy=lowercase__		).items()}     ,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}     ,     )
        self.assertEqual(map_nested(lowercase__     ,     lowercase__     ,     map_numpy=lowercase__     ,     num_proc=lowercase__		)     ,     lowercase__		)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(lowercase__     ,     lowercase__     ,     map_numpy=lowercase__     ,     num_proc=lowercase__		).items()}     ,     {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}     ,     )
        with self.assertRaises(lowercase__		):  # can't pickle a local lambda
              map_nested(lambda lowercase__		: x + 1     ,     lowercase__     ,     num_proc=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	List[str]					     =     {'''a''': 1, '''b''': 2}
        _snake_case		:	List[str]					     =     {'''a''': 3, '''b''': 4}
        _snake_case		:	str					     =     {'''a''': 5, '''b''': 6}
        _snake_case		:	Tuple					     =     sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]		)
        self.assertEqual(sorted(zip_dict(lowercase__     ,     lowercase__     ,     lowercase__		)		)     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        class lowerCamelCase						:
          _lowercase  :	Any       					=				'bar'



        _snake_case		:	Dict					     =     Foo()
        self.assertEqual(foo.my_attr     ,     '''bar'''		)
        with temporary_assignment(lowercase__     ,     '''my_attr'''     ,     '''BAR'''		):
              self.assertEqual(foo.my_attr     ,     '''BAR'''		)
        self.assertEqual(foo.my_attr     ,     '''bar'''		)




@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc'''			,      [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]			,      )
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      with patch('''datasets.utils.py_utils._single_map_nested'''	) as mock_single_map_nested, patch(
          '''datasets.parallel.parallel.Pool'''	) as mock_multiprocessing_pool:
            _snake_case		:	Optional[int]					     =     {f'''{i}''': i for i in range(_SCREAMING_SNAKE_CASE	)}
            _snake_case		:	Optional[Any]					     =     map_nested(lambda lowerCAmelCase_	: x + 10			,      _SCREAMING_SNAKE_CASE			,      num_proc=_SCREAMING_SNAKE_CASE			,      parallel_min_length=16	)
            if expected_num_proc == 1:
                  assert mock_single_map_nested.called
                  assert not mock_multiprocessing_pool.called
            else:
                  assert not mock_single_map_nested.called
                  assert mock_multiprocessing_pool.called
                  assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class lowerCamelCase						(a__					):







  @require_tf
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        import tensorflow as tf
        from tensorflow.keras import layers

        _snake_case		:	Optional[int]					     =     layers.Dense(2		)

        def gen_random_output():
              _snake_case		:	int					     =     tf.random.uniform((1, 3)		)
              return model(lowercase__		).numpy()

        with temp_seed(42     ,     set_tensorflow=lowercase__		):
              _snake_case		:	Union[str, Any]					     =     gen_random_output()
        with temp_seed(42     ,     set_tensorflow=lowercase__		):
              _snake_case		:	int					     =     gen_random_output()
        _snake_case		:	Tuple					     =     gen_random_output()

        np.testing.assert_equal(lowercase__     ,     lowercase__		)
        self.assertGreater(np.abs(outa - outa		).sum()     ,     0		)







  @require_torch
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        import torch

        def gen_random_output():
              _snake_case		:	Optional[int]					     =     torch.nn.Linear(3     ,     2		)
              _snake_case		:	Dict					     =     torch.rand(1     ,     3		)
              return model(lowercase__		).detach().numpy()

        with temp_seed(42     ,     set_pytorch=lowercase__		):
              _snake_case		:	Union[str, Any]					     =     gen_random_output()
        with temp_seed(42     ,     set_pytorch=lowercase__		):
              _snake_case		:	Optional[int]					     =     gen_random_output()
        _snake_case		:	str					     =     gen_random_output()

        np.testing.assert_equal(lowercase__     ,     lowercase__		)
        self.assertGreater(np.abs(outa - outa		).sum()     ,     0		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        def gen_random_output():
              return np.random.rand(1     ,     3		)

        with temp_seed(42		):
              _snake_case		:	Optional[int]					     =     gen_random_output()
        with temp_seed(42		):
              _snake_case		:	Dict					     =     gen_random_output()
        _snake_case		:	int					     =     gen_random_output()

        np.testing.assert_equal(lowercase__     ,     lowercase__		)
        self.assertGreater(np.abs(outa - outa		).sum()     ,     0		)




@pytest.mark.parametrize('''input_data'''			,      [{}]	)
def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     NestedDataStructure(_SCREAMING_SNAKE_CASE	).data
      assert output_data == input_data




@pytest.mark.parametrize(
    '''data, expected_output'''			,      [
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ]			,      )
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     NestedDataStructure(_SCREAMING_SNAKE_CASE	).flatten()
      assert output == expected_output




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[str]					     =     A(x=1			,      y='''foobar'''	)
      _snake_case		:	Optional[int]					     =     {'''x''': 1, '''y''': '''foobar'''}
      assert asdict(_SCREAMING_SNAKE_CASE	) == expected_output

      _snake_case		:	Optional[Any]					     =     {'''a''': {'''b''': A(x=10			,      y='''foo'''	)}, '''c''': [A(x=20			,      y='''bar'''	)]}
      _snake_case		:	Union[str, Any]					     =     {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
      assert asdict(_SCREAMING_SNAKE_CASE	) == expected_output

      with pytest.raises(_SCREAMING_SNAKE_CASE	):
            asdict([1, A(x=10			,      y='''foo'''	)]	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      return text.split()




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      yield (time.time(), content)
      time.sleep(2	)
      yield (time.time(), content)




def    _a    (							):





      """simple docstring"""



      with Pool(2	) as pool:
            _snake_case		:	Dict					     =     list(iflatmap_unordered(_SCREAMING_SNAKE_CASE			,      _split_text			,      kwargs_iterable=[{'''text''': '''hello there'''}] * 10	)	)
            assert out.count('''hello'''	) == 10
            assert out.count('''there'''	) == 10
            assert len(_SCREAMING_SNAKE_CASE	) == 20

      # check multiprocess from pathos (uses dill for pickling)
      with multiprocess.Pool(2	) as pool:
            _snake_case		:	Any					     =     list(iflatmap_unordered(_SCREAMING_SNAKE_CASE			,      _split_text			,      kwargs_iterable=[{'''text''': '''hello there'''}] * 10	)	)
            assert out.count('''hello'''	) == 10
            assert out.count('''there'''	) == 10
            assert len(_SCREAMING_SNAKE_CASE	) == 20

      # check that we get items as fast as possible
      with Pool(2	) as pool:
            _snake_case		:	Optional[Any]					     =     []
            for yield_time, content in iflatmap_unordered(
                _SCREAMING_SNAKE_CASE			,      _aseconds_generator_of_aitems_with_timing			,      kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]	):
                  assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                  out.append(_SCREAMING_SNAKE_CASE	)
            assert out.count('''a'''	) == 2
            assert out.count('''b'''	) == 2
            assert len(_SCREAMING_SNAKE_CASE	) == 4









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _in_place_partition(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      lowerCAmelCase_			,      p - 1	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      p + 1			,      lowerCAmelCase_	)
      return count




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      _snake_case		:	Optional[int]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     a[end]
      _snake_case		:	Optional[Any]					     =     a[pivot]
      _snake_case		:	Union[str, Any]					     =     temp
      _snake_case		:	Union[str, Any]					     =     start - 1
      for index in range(lowerCAmelCase_			,      lowerCAmelCase_	):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  _snake_case		:	Optional[int]					     =     new_pivot_index + 1
                  _snake_case		:	Optional[Any]					     =     a[new_pivot_index]
                  _snake_case		:	Tuple					     =     a[index]
                  _snake_case		:	str					     =     temp

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


UpperCAmelCase					:		Dict		=							TemporaryFile()
UpperCAmelCase					:		Dict		=							1_0_0  # 1000 elements are to be sorted


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		str		=							_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)


