
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 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any]  )   ->			Dict:  # picklable for multiprocessing






						'''simple docstring'''

						return x.sum()



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str]  )   ->			List[Any]:  # picklable for multiprocessing






						'''simple docstring'''

						return i + 1




@dataclass
class   __lowercase						:
	_A       =   42
	_A       =   42




class   __lowercase						(						__snake_case       ):






	def       _a(self				: Any    ) ->			int:
							_lowercase       :	Union[str, Any]           =   {}
							_lowercase       :	List[str]           =   []
							_lowercase       :	Tuple           =   1
							_lowercase       :	Optional[int]           =   [1, 2]
							_lowercase       :	Tuple           =   {"a": 1, "b": 2}
							_lowercase       :	int           =   {"a": [1, 2], "b": [3, 4]}
							_lowercase       :	Union[str, Any]           =   {"a": {"1": 1}, "b": 2}
							_lowercase       :	Optional[int]           =   {"a": 1, "b": 2, "c": 3, "d": 4}
							_lowercase       :	List[Any]           =   {}
							_lowercase       :	str           =   []
							_lowercase       :	Optional[int]           =   2
							_lowercase       :	List[str]           =   [2, 3]
							_lowercase       :	Optional[int]           =   {"a": 2, "b": 3}
							_lowercase       :	Optional[int]           =   {"a": [2, 3], "b": [4, 5]}
							_lowercase       :	int           =   {"a": {"1": 2}, "b": 3}
							_lowercase       :	Any           =   {"a": 2, "b": 3, "c": 4, "d": 5}
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case    )      ,	snake_case    )

							_lowercase       :	str           =   2
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )
							self.assertEqual(map_nested(snake_case      ,	snake_case      ,	num_proc=snake_case    )      ,	snake_case    )

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






	def       _a(self				: Optional[int]    ) ->			Tuple:
							_lowercase       :	Optional[Any]           =   {"a": 1, "b": 2}
							_lowercase       :	List[str]           =   {"a": 3, "b": 4}
							_lowercase       :	Tuple           =   {"a": 5, "b": 6}
							_lowercase       :	List[Any]           =   sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]    )
							self.assertEqual(sorted(zip_dict(snake_case      ,	snake_case      ,	snake_case    )    )      ,	snake_case    )






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




							class   __lowercase						:
								_A       =   "bar"


							_lowercase       :	Any           =   Foo()
							self.assertEqual(foo.my_attr      ,	"bar"    )
							with temporary_assignment(snake_case      ,	"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 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Optional[Any]  )   ->			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       :	List[Any]           =   {f"""{i}""": i for i in range(_UpperCAmelCase  )}
												_lowercase       :	str           =   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   __lowercase						(						__snake_case       ):






	@require_tf
	def       _a(self				: Dict    ) ->			int:
							import tensorflow as tf
							from tensorflow.keras import layers

							_lowercase       :	Any           =   layers.Dense(2    )

							def gen_random_output():
													_lowercase       :	List[Any]           =   tf.random.uniform((1, 3)    )
													return model(snake_case    ).numpy()

							with temp_seed(42      ,	set_tensorflow=snake_case    ):
													_lowercase       :	Union[str, Any]           =   gen_random_output()
							with temp_seed(42      ,	set_tensorflow=snake_case    ):
													_lowercase       :	List[str]           =   gen_random_output()
							_lowercase       :	Tuple           =   gen_random_output()

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






	@require_torch
	def       _a(self				: Tuple    ) ->			Dict:
							import torch

							def gen_random_output():
													_lowercase       :	List[Any]           =   torch.nn.Linear(3      ,	2    )
													_lowercase       :	int           =   torch.rand(1      ,	3    )
													return model(snake_case    ).detach().numpy()

							with temp_seed(42      ,	set_pytorch=snake_case    ):
													_lowercase       :	int           =   gen_random_output()
							with temp_seed(42      ,	set_pytorch=snake_case    ):
													_lowercase       :	Optional[Any]           =   gen_random_output()
							_lowercase       :	Any           =   gen_random_output()

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






	def       _a(self				: Any    ) ->			Any:
							def gen_random_output():
													return np.random.rand(1      ,	3    )

							with temp_seed(42    ):
													_lowercase       :	Optional[int]           =   gen_random_output()
							with temp_seed(42    ):
													_lowercase       :	Optional[int]           =   gen_random_output()
							_lowercase       :	Any           =   gen_random_output()

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



@pytest.mark.parametrize("input_data" ,			[{}]  )
def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   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 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   Any  )   ->			Union[str, Any]:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   NestedDataStructure(_UpperCAmelCase  ).flatten()
						assert output == expected_output



def 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   A(x=1 ,			y="foobar"  )
						_lowercase       :	Union[str, Any]           =   {"x": 1, "y": "foobar"}
						assert asdict(_UpperCAmelCase  ) == expected_output

						_lowercase       :	Optional[Any]           =   {"a": {"b": A(x=10 ,			y="foo"  )}, "c": [A(x=20 ,			y="bar"  )]}
						_lowercase       :	Optional[Any]           =   {"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 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			List[Any]:






						'''simple docstring'''

						return text.split()



def 		UpperCamelCase     (       _UpperCAmelCase  :   Tuple  )   ->			Dict:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			List[Any]:






						'''simple docstring'''

						with Pool(2  ) as pool:
												_lowercase       :	Dict           =   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:
												_lowercase       :	Optional[int]           =   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:
												_lowercase       :	Tuple           =   []
												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






def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			bool:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [int(_UpperCAmelCase  ) for i in ip_va_address.split("."  ) if i.isdigit()]
						return len(_UpperCAmelCase  ) == 4 and all(0 <= int(_UpperCAmelCase  ) <= 254 for octet in octets  )


if __name__ == "__main__":
							UpperCamelCase_						:     Dict	       =							input().strip()
							UpperCamelCase_						:     List[Any]	       =							"""valid""" if is_ip_va_address_valid(ip) else """invalid"""
							print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')




