


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 			_lowerCamelCase(      lowercase__      )		->      Any:  # picklable for multiprocessing

		'''simple docstring'''







		return x.sum()







def 			_lowerCamelCase(      lowercase__      )		->      str:  # picklable for multiprocessing

		'''simple docstring'''







		return i + 1





@dataclass
class  A       :
							UpperCamelCase_  :				int
							UpperCamelCase_  :				str





class  A       (  A_					):


							def      _A			(self   ):
									__lowercase=     {}
									__lowercase=     []
									__lowercase=     1
									__lowercase=     [1, 2]
									__lowercase=     {"""a""": 1, """b""": 2}
									__lowercase=     {"""a""": [1, 2], """b""": [3, 4]}
									__lowercase=     {"""a""": {"""1""": 1}, """b""": 2}
									__lowercase=     {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
									__lowercase=     {}
									__lowercase=     []
									__lowercase=     2
									__lowercase=     [2, 3]
									__lowercase=     {"""a""": 2, """b""": 3}
									__lowercase=     {"""a""": [2, 3], """b""": [4, 5]}
									__lowercase=     {"""a""": {"""1""": 2}, """b""": 3}
									__lowercase=     {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__   ) ,							A__   )

									__lowercase=     2
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(map_nested(A__ ,							A__ ,							num_proc=A__   ) ,							A__   )

									__lowercase=     {"""a""": np.eye(2   ), """b""": np.zeros(3   ), """c""": np.ones(2   )}
									__lowercase=     {"""a""": 2, """b""": 0, """c""": 2}
									__lowercase=     {
									    """a""": np.eye(2   ).astype(A__   ),
									    """b""": np.zeros(3   ).astype(A__   ),
									    """c""": np.ones(2   ).astype(A__   ),
									}
									self.assertEqual(map_nested(A__ ,							A__ ,							map_numpy=A__   ) ,							A__   )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(A__ ,							A__ ,							map_numpy=A__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
									self.assertEqual(map_nested(A__ ,							A__ ,							map_numpy=A__ ,							num_proc=A__   ) ,							A__   )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(A__ ,							A__ ,							map_numpy=A__ ,							num_proc=A__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
									with self.assertRaises(A__   ):  # can't pickle a local lambda
											map_nested(lambda lowerCAmelCase   : x + 1 ,							A__ ,							num_proc=A__   )


							def      _A			(self   ):
									__lowercase=     {"""a""": 1, """b""": 2}
									__lowercase=     {"""a""": 3, """b""": 4}
									__lowercase=     {"""a""": 5, """b""": 6}
									__lowercase=     sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]   )
									self.assertEqual(sorted(zip_dict(A__ ,							A__ ,							A__   )   ) ,							A__   )







							def      _A			(self   ):





									class  A       :
																UpperCamelCase_  :				int			='''bar'''


									__lowercase=     Foo()
									self.assertEqual(foo.my_attr ,							'bar'   )
									with temporary_assignment(A__ ,							'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 			_lowerCamelCase(      lowercase__  ,					lowercase__  ,					lowercase__      )		->      Optional[int]:

		'''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:
				__lowercase=     {F'{i}': i for i in range(lowercase_      )}
				__lowercase=     map_nested(lambda lowercase__      : x + 1_0  ,					lowercase_  ,					num_proc=lowercase_  ,					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  A       (  A_					):


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

									__lowercase=     layers.Dense(2   )

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

									with temp_seed(4_2 ,							set_tensorflow=A__   ):
											__lowercase=     gen_random_output()
									with temp_seed(4_2 ,							set_tensorflow=A__   ):
											__lowercase=     gen_random_output()
									__lowercase=     gen_random_output()

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


							@require_torch
							def      _A			(self   ):
									import torch

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

									with temp_seed(4_2 ,							set_pytorch=A__   ):
											__lowercase=     gen_random_output()
									with temp_seed(4_2 ,							set_pytorch=A__   ):
											__lowercase=     gen_random_output()
									__lowercase=     gen_random_output()

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







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

									with temp_seed(4_2   ):
											__lowercase=     gen_random_output()
									with temp_seed(4_2   ):
											__lowercase=     gen_random_output()
									__lowercase=     gen_random_output()

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







@pytest.mark.parametrize('input_data'  ,					[{}]      )
def 			_lowerCamelCase(      lowercase__      )		->      Tuple:

		'''simple docstring'''







		__lowercase=     NestedDataStructure(lowercase_      ).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 			_lowerCamelCase(      lowercase__  ,					lowercase__      )		->      int:

		'''simple docstring'''







		__lowercase=     NestedDataStructure(lowercase_      ).flatten()
		assert output == expected_output







def 			_lowerCamelCase(      )		->      Any:

		'''simple docstring'''







		__lowercase=     A(x=1  ,					y='foobar'      )
		__lowercase=     {"""x""": 1, """y""": """foobar"""}
		assert asdict(lowercase_      ) == expected_output

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

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







def 			_lowerCamelCase(      lowercase__      )		->      Tuple:

		'''simple docstring'''







		return text.split()







def 			_lowerCamelCase(      lowercase__      )		->      Optional[int]:

		'''simple docstring'''







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







def 			_lowerCamelCase(      )		->      Dict:

		'''simple docstring'''







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

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

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









def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



