
"""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						(   a_ :							int     )->							Optional[Any]:  # picklable for multiprocessing




		'''simple docstring'''






		return x.sum()





def __A						(   a_ :							Union[str, Any]     )->							List[Any]:  # picklable for multiprocessing




		'''simple docstring'''






		return i + 1




@dataclass
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  42
	UpperCamelCase				=  42





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[Any]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Any				= 2
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)
			self.assertEqual(map_nested(lowerCamelCase_		, lowerCamelCase_		, num_proc=lowerCamelCase_					)		, lowerCamelCase_					)

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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= {'''a''': 1, '''b''': 2}
			SCREAMING_SNAKE_CASE       :     Optional[int]				= {'''a''': 3, '''b''': 4}
			SCREAMING_SNAKE_CASE       :     Tuple				= {'''a''': 5, '''b''': 6}
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]					)
			self.assertEqual(sorted(zip_dict(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)					)		, lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :int					)							->			str:







			'''simple docstring'''




			class 				lowercase__:
				'''simple docstring'''

				UpperCamelCase				=  """bar"""





			SCREAMING_SNAKE_CASE       :     List[str]				= Foo()
			self.assertEqual(foo.my_attr		, '''bar'''					)
			with temporary_assignment(lowerCamelCase_		, '''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						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							List[Any]     )->							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:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {F"{i}": i for i in range(a_     )}
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= map_nested(lambda a_     : x + 10		,		a_		,		num_proc=a_		,		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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@require_tf
	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			import tensorflow as tf
			from tensorflow.keras import layers

			SCREAMING_SNAKE_CASE       :     List[Any]				= layers.Dense(2					)

			def gen_random_output():
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= tf.random.uniform((1, 3)					)
					return model(lowerCamelCase_					).numpy()

			with temp_seed(42		, set_tensorflow=lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     List[str]				= gen_random_output()
			with temp_seed(42		, set_tensorflow=lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Optional[int]				= gen_random_output()
			SCREAMING_SNAKE_CASE       :     List[str]				= gen_random_output()

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





	@require_torch
	def   __lowerCAmelCase (						self    :str					)							->			int:







			'''simple docstring'''




			import torch

			def gen_random_output():
					SCREAMING_SNAKE_CASE       :     List[str]				= torch.nn.Linear(3		, 2					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.rand(1		, 3					)
					return model(lowerCamelCase_					).detach().numpy()

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

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







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







			'''simple docstring'''




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

			with temp_seed(42					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= gen_random_output()
			with temp_seed(42					):
					SCREAMING_SNAKE_CASE       :     Any				= gen_random_output()
			SCREAMING_SNAKE_CASE       :     Tuple				= gen_random_output()

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





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




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= NestedDataStructure(a_     ).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						(   a_ :							Dict		,		a_ :							Any     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= NestedDataStructure(a_     ).flatten()
		assert output == expected_output





def __A						(   )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= A(x=1		,		y='''foobar'''     )
		SCREAMING_SNAKE_CASE       :     Dict				= {'''x''': 1, '''y''': '''foobar'''}
		assert asdict(a_     ) == expected_output

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

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





def __A						(   a_ :							str     )->							List[str]:




		'''simple docstring'''






		return text.split()





def __A						(   a_ :							int     )->							Union[str, Any]:




		'''simple docstring'''






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





def __A						(   )->							Dict:




		'''simple docstring'''






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

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

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







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


if __name__ == "__main__":
					print(solution())





