


'''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__					:str						)				->					int:  # picklable for multiprocessing




     '''simple docstring'''



     return x.sum()






def 		_A			(lowerCAmelCase__					:int						)				->					int:  # picklable for multiprocessing




     '''simple docstring'''



     return i + 1




@dataclass
class 	a       :
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		42





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							{}
      _a      =							[]
      _a      =							1
      _a      =							[1, 2]
      _a      =							{'a': 1, 'b': 2}
      _a      =							{'a': [1, 2], 'b': [3, 4]}
      _a      =							{'a': {'1': 1}, 'b': 2}
      _a      =							{'a': 1, 'b': 2, 'c': 3, 'd': 4}
      _a      =							{}
      _a      =							[]
      _a      =							2
      _a      =							[2, 3]
      _a      =							{'a': 2, 'b': 3}
      _a      =							{'a': [2, 3], 'b': [4, 5]}
      _a      =							{'a': {'1': 2}, 'b': 3}
      _a      =							{'a': 2, 'b': 3, 'c': 4, 'd': 5}
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__  ) ,			__magic_name__  )

      _a      =							2
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )

      _a      =							{'a': np.eye(2  ), 'b': np.zeros(3  ), 'c': np.ones(2  )}
      _a      =							{'a': 2, 'b': 0, 'c': 2}
      _a      =							{
          'a': np.eye(2  ).astype(__magic_name__  ),
          'b': np.zeros(3  ).astype(__magic_name__  ),
          'c': np.ones(2  ).astype(__magic_name__  ),
      }
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			map_numpy=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(__magic_name__ ,			__magic_name__ ,			map_numpy=__magic_name__  ).items()} ,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,			)
      self.assertEqual(map_nested(__magic_name__ ,			__magic_name__ ,			map_numpy=__magic_name__ ,			num_proc=__magic_name__  ) ,			__magic_name__  )
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(__magic_name__ ,			__magic_name__ ,			map_numpy=__magic_name__ ,			num_proc=__magic_name__  ).items()} ,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,			)
      with self.assertRaises(__magic_name__  ):  # can't pickle a local lambda
           map_nested(lambda __magic_name__  : x + 1 ,			__magic_name__ ,			num_proc=__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							{'a': 1, 'b': 2}
      _a      =							{'a': 3, 'b': 4}
      _a      =							{'a': 5, 'b': 6}
      _a      =							sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]  )
      self.assertEqual(sorted(zip_dict(__magic_name__ ,			__magic_name__ ,			__magic_name__  )  ) ,			__magic_name__  )




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:




      class 	a       :
       _lowerCAmelCase	   =		"""bar"""





      _a      =							Foo()
      self.assertEqual(foo.my_attr ,			'bar'  )
      with temporary_assignment(__magic_name__ ,			'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__					:int			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:str						)				->					str:




     '''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:
          _a      =							{f'{i}': i for i in range(lowerCAmelCase__						)}
          _a      =							map_nested(lambda lowerCAmelCase__						: x + 10			,  lowerCAmelCase__			,  num_proc=lowerCAmelCase__			,  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 	a       (       _SCREAMING_SNAKE_CASE    ):
 @require_tf
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      import tensorflow as tf
      from tensorflow.keras import layers

      _a      =							layers.Dense(2  )

      def gen_random_output():
           _a      =							tf.random.uniform((1, 3)  )
           return model(__magic_name__  ).numpy()

      with temp_seed(42 ,			set_tensorflow=__magic_name__  ):
           _a      =							gen_random_output()
      with temp_seed(42 ,			set_tensorflow=__magic_name__  ):
           _a      =							gen_random_output()
      _a      =							gen_random_output()

      np.testing.assert_equal(__magic_name__ ,			__magic_name__  )
      self.assertGreater(np.abs(outa - outa  ).sum() ,			0  )
 @require_torch
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      import torch

      def gen_random_output():
           _a      =							torch.nn.Linear(3 ,			2  )
           _a      =							torch.rand(1 ,			3  )
           return model(__magic_name__  ).detach().numpy()

      with temp_seed(42 ,			set_pytorch=__magic_name__  ):
           _a      =							gen_random_output()
      with temp_seed(42 ,			set_pytorch=__magic_name__  ):
           _a      =							gen_random_output()
      _a      =							gen_random_output()

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




 def 							__UpperCAmelCase						(	self  )   ->							str:
      def gen_random_output():
           return np.random.rand(1 ,			3  )

      with temp_seed(42  ):
           _a      =							gen_random_output()
      with temp_seed(42  ):
           _a      =							gen_random_output()
      _a      =							gen_random_output()

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






@pytest.mark.parametrize('input_data'			,  [{}]						)
def 		_A			(lowerCAmelCase__					:Optional[int]						)				->					List[str]:




     '''simple docstring'''



     _a      =							NestedDataStructure(lowerCAmelCase__						).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__					:Optional[int]			,  lowerCAmelCase__					:List[str]						)				->					str:




     '''simple docstring'''



     _a      =							NestedDataStructure(lowerCAmelCase__						).flatten()
     assert output == expected_output






def 		_A			()				->					str:




     '''simple docstring'''



     _a      =							A(x=1			,  y='foobar'						)
     _a      =							{'x': 1, 'y': 'foobar'}
     assert asdict(lowerCAmelCase__						) == expected_output

     _a      =							{'a': {'b': A(x=10			,  y='foo'						)}, 'c': [A(x=20			,  y='bar'						)]}
     _a      =							{'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
     assert asdict(lowerCAmelCase__						) == expected_output

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






def 		_A			(lowerCAmelCase__					:str						)				->					Union[str, Any]:




     '''simple docstring'''



     return text.split()






def 		_A			(lowerCAmelCase__					:Dict						)				->					str:




     '''simple docstring'''



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






def 		_A			()				->					Union[str, Any]:




     '''simple docstring'''



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

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

     # check that we get items as fast as possible
     with Pool(2						) as pool:
          _a      =							[]
          for yield_time, content in iflatmap_unordered(
              lowerCAmelCase__			,  _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(lowerCAmelCase__						)
          assert out.count('a'						) == 2
          assert out.count('b'						) == 2
          assert len(lowerCAmelCase__						) == 4





'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

