

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(lowercase__						):  # picklable for multiprocessing





	'''simple docstring'''
	return x.sum()





def a(lowercase__						):  # picklable for multiprocessing





	'''simple docstring'''
	return i + 1


@dataclass
class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			__A    	=	42
			__A    	=	42







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			{}
				snake_case_   		=			[]
				snake_case_   		=			1
				snake_case_   		=			[1, 2]
				snake_case_   		=			{'a': 1, 'b': 2}
				snake_case_   		=			{'a': [1, 2], 'b': [3, 4]}
				snake_case_   		=			{'a': {'1': 1}, 'b': 2}
				snake_case_   		=			{'a': 1, 'b': 2, 'c': 3, 'd': 4}
				snake_case_   		=			{}
				snake_case_   		=			[]
				snake_case_   		=			2
				snake_case_   		=			[2, 3]
				snake_case_   		=			{'a': 2, 'b': 3}
				snake_case_   		=			{'a': [2, 3], 'b': [4, 5]}
				snake_case_   		=			{'a': {'1': 2}, 'b': 3}
				snake_case_   		=			{'a': 2, 'b': 3, 'c': 4, 'd': 5}
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase				)			,					__UpperCamelCase				)

				snake_case_   		=			2
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)
				self.assertEqual(map_nested(__UpperCamelCase			,					__UpperCamelCase			,					num_proc=__UpperCamelCase				)			,					__UpperCamelCase				)

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			{'a': 1, 'b': 2}
				snake_case_   		=			{'a': 3, 'b': 4}
				snake_case_   		=			{'a': 5, 'b': 6}
				snake_case_   		=			sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]				)
				self.assertEqual(sorted(zip_dict(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)				)			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				class SCREAMING_SNAKE_CASE	:






							"""simple docstring"""




							__A    	=	"""bar"""







				snake_case_   		=			Foo()
				self.assertEqual(foo.my_attr			,					'bar'				)
				with temporary_assignment(__UpperCamelCase			,					'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(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''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_   		=			{f"""{i}""": i for i in range(lowercase__						)}
		snake_case_   		=			map_nested(lambda lowercase__						: x + 10      ,			lowercase__      ,			num_proc=lowercase__      ,			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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@require_tf
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				import tensorflow as tf
				from tensorflow.keras import layers

				snake_case_   		=			layers.Dense(2				)

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

				with temp_seed(42			,					set_tensorflow=__UpperCamelCase				):
					snake_case_   		=			gen_random_output()
				with temp_seed(42			,					set_tensorflow=__UpperCamelCase				):
					snake_case_   		=			gen_random_output()
				snake_case_   		=			gen_random_output()

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


			@require_torch
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				import torch

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

				with temp_seed(42			,					set_pytorch=__UpperCamelCase				):
					snake_case_   		=			gen_random_output()
				with temp_seed(42			,					set_pytorch=__UpperCamelCase				):
					snake_case_   		=			gen_random_output()
				snake_case_   		=			gen_random_output()

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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

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







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





	'''simple docstring'''
	snake_case_   		=			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 a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			NestedDataStructure(lowercase__						).flatten()
	assert output == expected_output





def a():





	'''simple docstring'''
	snake_case_   		=			A(x=1      ,			y='foobar'						)
	snake_case_   		=			{'x': 1, 'y': 'foobar'}
	assert asdict(lowercase__						) == expected_output

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

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





def a(lowercase__						):





	'''simple docstring'''
	return text.split()





def a(lowercase__						):





	'''simple docstring'''
	yield (time.time(), content)
	time.sleep(2						)
	yield (time.time(), content)






def a():





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

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

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



from __future__ import annotations





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if (direction == 1 and array[indexa] > array[indexa]) or (
	    direction == 0 and array[indexa] < array[indexa]
	):
		snake_case_		,							snake_case_   		=			array[indexa], array[indexa]





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		for i in range(lowercase__      ,			low + middle						):
			comp_and_swap(lowercase__      ,			lowercase__      ,			i + middle      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			low + middle      ,			lowercase__      ,			lowercase__						)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		bitonic_sort(lowercase__      ,			lowercase__      ,			lowercase__      ,			1						)
		bitonic_sort(lowercase__      ,			low + middle      ,			lowercase__      ,			0						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)


if __name__ == "__main__":
		A									=  input('Enter numbers separated by a comma:\n').strip()
		A									=  [int(item.strip()) for item in user_input.split(',')]

		bitonic_sort(unsorted, 0, len(unsorted), 1)
		print('\nSorted array in ascending order is: ', end='')
		print(*unsorted, sep=', ')

		bitonic_merge(unsorted, 0, len(unsorted), 0)
		print('Sorted array in descending order is: ', end='')
		print(*unsorted, sep=', ')
