"""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
				return x.sum()



def   a_						(					lowerCamelCase       ):  # picklable for multiprocessing
				return i + 1
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = 42






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							{}
									UpperCAmelCase__             =							[]
									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							[1, 2]
									UpperCAmelCase__             =							{'a': 1, 'b': 2}
									UpperCAmelCase__             =							{'a': [1, 2], 'b': [3, 4]}
									UpperCAmelCase__             =							{'a': {'1': 1}, 'b': 2}
									UpperCAmelCase__             =							{'a': 1, 'b': 2, 'c': 3, 'd': 4}
									UpperCAmelCase__             =							{}
									UpperCAmelCase__             =							[]
									UpperCAmelCase__             =							2
									UpperCAmelCase__             =							[2, 3]
									UpperCAmelCase__             =							{'a': 2, 'b': 3}
									UpperCAmelCase__             =							{'a': [2, 3], 'b': [4, 5]}
									UpperCAmelCase__             =							{'a': {'1': 2}, 'b': 3}
									UpperCAmelCase__             =							{'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__			)

									UpperCAmelCase__             =							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__			)

									UpperCAmelCase__             =							{'a': np.eye(2			), 'b': np.zeros(3			), 'c': np.ones(2			)}
									UpperCAmelCase__             =							{'a': 2, 'b': 0, 'c': 2}
									UpperCAmelCase__             =							{
									    '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   :	Any			):
									UpperCAmelCase__             =							{'a': 1, 'b': 2}
									UpperCAmelCase__             =							{'a': 3, 'b': 4}
									UpperCAmelCase__             =							{'a': 5, 'b': 6}
									UpperCAmelCase__             =							sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]			)
									self.assertEqual(sorted(zip_dict(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									class 				snake_case       :




														"""simple docstring"""

														snake_case__				    = "bar"






									UpperCAmelCase__             =							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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]					,			)
def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				with patch('datasets.utils.py_utils._single_map_nested'       ) as mock_single_map_nested, patch(
				    'datasets.parallel.parallel.Pool'       ) as mock_multiprocessing_pool:
								UpperCAmelCase__             =							{f'''{i}''': i for i in range(lowerCamelCase       )}
								UpperCAmelCase__             =							map_nested(lambda lowerCamelCase       : x + 1_0					,			lowerCamelCase					,			num_proc=lowerCamelCase					,			parallel_min_length=1_6       )
								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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					@require_tf
					def 		__lowerCAmelCase						(				self   :	Any			):
									import tensorflow as tf
									from tensorflow.keras import layers

									UpperCAmelCase__             =							layers.Dense(2			)

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

									with temp_seed(42    ,set_tensorflow=lowerCamelCase__			):
													UpperCAmelCase__             =							gen_random_output()
									with temp_seed(42    ,set_tensorflow=lowerCamelCase__			):
													UpperCAmelCase__             =							gen_random_output()
									UpperCAmelCase__             =							gen_random_output()

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







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Dict			):
									import torch

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

									with temp_seed(42    ,set_pytorch=lowerCamelCase__			):
													UpperCAmelCase__             =							gen_random_output()
									with temp_seed(42    ,set_pytorch=lowerCamelCase__			):
													UpperCAmelCase__             =							gen_random_output()
									UpperCAmelCase__             =							gen_random_output()

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







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

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

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






@pytest.mark.parametrize('input_data'					,			[{}]       )
def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							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					,			lowerCamelCase       ):
				UpperCAmelCase__             =							NestedDataStructure(lowerCamelCase       ).flatten()
				assert output == expected_output



def   a_						(					):
				UpperCAmelCase__             =							A(x=1					,			y='foobar'       )
				UpperCAmelCase__             =							{'x': 1, 'y': 'foobar'}
				assert asdict(lowerCamelCase       ) == expected_output

				UpperCAmelCase__             =							{'a': {'b': A(x=1_0					,			y='foo'       )}, 'c': [A(x=2_0					,			y='bar'       )]}
				UpperCAmelCase__             =							{'a': {'b': {'x': 1_0, 'y': 'foo'}}, 'c': [{'x': 2_0, 'y': 'bar'}]}
				assert asdict(lowerCamelCase       ) == expected_output

				with pytest.raises(lowerCamelCase       ):
								asdict([1, A(x=1_0					,			y='foo'       )]       )



def   a_						(					lowerCamelCase       ):
				return text.split()



def   a_						(					lowerCamelCase       ):
				yield (time.time(), content)
				time.sleep(2       )
				yield (time.time(), content)







def   a_						(					):
				with Pool(2       ) as pool:
								UpperCAmelCase__             =							list(iflatmap_unordered(lowerCamelCase					,			_split_text					,			kwargs_iterable=[{'text': 'hello there'}] * 1_0       )       )
								assert out.count('hello'       ) == 1_0
								assert out.count('there'       ) == 1_0
								assert len(lowerCamelCase       ) == 2_0

				# check multiprocess from pathos (uses dill for pickling)
				with multiprocess.Pool(2       ) as pool:
								UpperCAmelCase__             =							list(iflatmap_unordered(lowerCamelCase					,			_split_text					,			kwargs_iterable=[{'text': 'hello there'}] * 1_0       )       )
								assert out.count('hello'       ) == 1_0
								assert out.count('there'       ) == 1_0
								assert len(lowerCamelCase       ) == 2_0

				# check that we get items as fast as possible
				with Pool(2       ) as pool:
								UpperCAmelCase__             =							[]
								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

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = None
					snake_case__				    = None






lowerCAmelCase__  :  Union[str, Any]						     =	namedtuple('CoinsDistribResult', 'moves excess')



def   a_						(					lowerCamelCase       ):

				if root is None:
								return 0

				# Validation
				def count_nodes(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_nodes(node.left       ) + count_nodes(node.right       ) + 1

				def count_coins(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_coins(node.left       ) + count_coins(node.right       ) + node.data

				if count_nodes(lowerCamelCase       ) != count_coins(lowerCamelCase       ):
								raise ValueError('The nodes number should be same as the number of coins'       )

				# Main calculation
				def get_distrib(lowerCamelCase       ) -> CoinsDistribResult:

								if node is None:
												return CoinsDistribResult(0					,			1       )

								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.left       )
								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.right       )

								UpperCAmelCase__             =							1 - left_distrib_excess
								UpperCAmelCase__             =							1 - right_distrib_excess

								UpperCAmelCase__             =							(
								    left_distrib_moves
								    + right_distrib_moves
								    + abs(lowerCamelCase       )
								    + abs(lowerCamelCase       )
								)
								UpperCAmelCase__             =							node.data - coins_to_left - coins_to_right

								return CoinsDistribResult(lowerCamelCase					,			lowerCamelCase       )

				return get_distrib(lowerCamelCase       )[0]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



