





"""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__				(			UpperCamelCase       ):  # picklable for multiprocessing
							return x.sum()






def 					A__				(			UpperCamelCase       ):  # picklable for multiprocessing
							return i + 1






@dataclass
class 					_UpperCAmelCase   :
					UpperCamelCase      =							42
					UpperCamelCase      =							42







class 					_UpperCAmelCase   (	lowercase_ ):


					def 	lowerCamelCase       (					self			: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(__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		)

												A		=  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		)

												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(__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			:Dict		):
												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(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]		):






												class 					_UpperCAmelCase   :
																	UpperCamelCase      =							'''bar'''







												A		=  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__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							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(UpperCamelCase       )}
														A		=  map_nested(lambda UpperCamelCase       : x + 10					,  UpperCamelCase					,  num_proc=UpperCamelCase					,  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 					_UpperCAmelCase   (	lowercase_ ):


					@require_tf
					def 	lowerCamelCase       (					self			:str		):
												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(__UpperCamelCase		).numpy()

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

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


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[int]		):
												import torch

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

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

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


					def 	lowerCamelCase       (					self			:Tuple		):
												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(__UpperCamelCase     ,       __UpperCamelCase		)
												self.assertGreater(np.abs(outa - outa		).sum()     ,       0		)







@pytest.mark.parametrize("input_data"					,  [{}]       )
def 					A__				(			UpperCamelCase       ):
							A		=  NestedDataStructure(UpperCamelCase       ).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__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  NestedDataStructure(UpperCamelCase       ).flatten()
							assert output == expected_output






def 					A__				(			):
							A		=  A(x=1					,  y="foobar"       )
							A		=  {"x": 1, "y": "foobar"}
							assert asdict(UpperCamelCase       ) == 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(UpperCamelCase       ) == expected_output

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






def 					A__				(			UpperCamelCase       ):
							return text.split()






def 					A__				(			UpperCamelCase       ):
							yield (time.time(), content)
							time.sleep(2       )
							yield (time.time(), content)






def 					A__				(			):
							with Pool(2       ) as pool:
														A		=  list(iflatmap_unordered(UpperCamelCase					,  _split_text					,  kwargs_iterable=[{"text": "hello there"}] * 10       )       )
														assert out.count("hello"       ) == 10
														assert out.count("there"       ) == 10
														assert len(UpperCamelCase       ) == 20

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

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









"""simple docstring"""
from math import isqrt, loga






def 					A__				(			UpperCamelCase       ):
							A		=  [True] * max_number
							for i in range(2					,  isqrt(max_number - 1       ) + 1       ):
														if is_prime[i]:
																					for j in range(i**2					,  UpperCamelCase					,  UpperCamelCase       ):
																												A		=  False

							return [i for i in range(2					,  UpperCamelCase       ) if is_prime[i]]






def 					A__				(			UpperCamelCase = 800_800					,  UpperCamelCase = 800_800       ):
							A		=  degree * loga(UpperCamelCase       )
							A		=  int(UpperCamelCase       )
							A		=  calculate_prime_numbers(UpperCamelCase       )

							A		=  0
							A		=  0
							A		=  len(UpperCamelCase       ) - 1
							while left < right:
														while (
														    prime_numbers[right] * loga(prime_numbers[left]       )
														    + prime_numbers[left] * loga(prime_numbers[right]       )
														    > upper_bound
														):
																					right -= 1
														hybrid_integers_count += right - left
														left += 1

							return hybrid_integers_count


if __name__ == "__main__":
		print(F"""{solution() = }""")


