





'''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(SCREAMING_SNAKE_CASE_  :					List[Any]    ):  # picklable for multiprocessing



    '''simple docstring'''





    return x.sum()


def 	__a(SCREAMING_SNAKE_CASE_  :					Any    ):  # picklable for multiprocessing



    '''simple docstring'''





    return i + 1

@dataclass
class    lowerCAmelCase_		:
 __lowerCamelCase						:							int
 __lowerCamelCase						:							str







class    lowerCAmelCase_		(		__magic_name__		):




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      {}
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      1
     _lowerCAmelCase				=      [1, 2]
     _lowerCAmelCase				=      {"a": 1, "b": 2}
     _lowerCAmelCase				=      {"a": [1, 2], "b": [3, 4]}
     _lowerCAmelCase				=      {"a": {"1": 1}, "b": 2}
     _lowerCAmelCase				=      {"a": 1, "b": 2, "c": 3, "d": 4}
     _lowerCAmelCase				=      {}
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      2
     _lowerCAmelCase				=      [2, 3]
     _lowerCAmelCase				=      {"a": 2, "b": 3}
     _lowerCAmelCase				=      {"a": [2, 3], "b": [4, 5]}
     _lowerCAmelCase				=      {"a": {"1": 2}, "b": 3}
     _lowerCAmelCase				=      {"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		)

     _lowerCAmelCase				=      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		)

     _lowerCAmelCase				=      {"a": np.eye(2		), "b": np.zeros(3		), "c": np.ones(2		)}
     _lowerCAmelCase				=      {"a": 2, "b": 0, "c": 2}
     _lowerCAmelCase				=      {
         "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 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      {"a": 1, "b": 2}
     _lowerCAmelCase				=      {"a": 3, "b": 4}
     _lowerCAmelCase				=      {"a": 5, "b": 6}
     _lowerCAmelCase				=      sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]		)
     self.assertEqual(sorted(zip_dict(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)		)			,     _lowerCAmelCase		)





 def 							_snake_case   (	self		)    ->							Optional[int]:

     class    lowerCAmelCase_		:
      __lowerCamelCase						:							int         =					"bar"







     _lowerCAmelCase				=      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),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]		,       )
def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''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:
        _lowerCAmelCase				=      {F'''{i}''': i for i in range(SCREAMING_SNAKE_CASE_    )}
        _lowerCAmelCase				=      map_nested(lambda SCREAMING_SNAKE_CASE_    : x + 10		,       SCREAMING_SNAKE_CASE_		,       num_proc=SCREAMING_SNAKE_CASE_		,       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    lowerCAmelCase_		(		__magic_name__		):




 @require_tf
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     import tensorflow as tf
     from tensorflow.keras import layers

     _lowerCAmelCase				=      layers.Dense(2		)

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

     with temp_seed(42			,     set_tensorflow=_lowerCAmelCase		):
         _lowerCAmelCase				=      gen_random_output()
     with temp_seed(42			,     set_tensorflow=_lowerCAmelCase		):
         _lowerCAmelCase				=      gen_random_output()
     _lowerCAmelCase				=      gen_random_output()

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




 @require_torch
 def 							_snake_case   (	self		)    ->							Optional[int]:
     import torch

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

     with temp_seed(42			,     set_pytorch=_lowerCAmelCase		):
         _lowerCAmelCase				=      gen_random_output()
     with temp_seed(42			,     set_pytorch=_lowerCAmelCase		):
         _lowerCAmelCase				=      gen_random_output()
     _lowerCAmelCase				=      gen_random_output()

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





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

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

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







@pytest.mark.parametrize("input_data"		,       [{}]    )
def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    _lowerCAmelCase				=      NestedDataStructure(SCREAMING_SNAKE_CASE_    ).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(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Optional[Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      NestedDataStructure(SCREAMING_SNAKE_CASE_    ).flatten()
    assert output == expected_output


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      A(x=1		,       y="foobar"    )
    _lowerCAmelCase				=      {"x": 1, "y": "foobar"}
    assert asdict(SCREAMING_SNAKE_CASE_    ) == expected_output

    _lowerCAmelCase				=      {"a": {"b": A(x=10		,       y="foo"    )}, "c": [A(x=20		,       y="bar"    )]}
    _lowerCAmelCase				=      {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
    assert asdict(SCREAMING_SNAKE_CASE_    ) == expected_output

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


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    return text.split()


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





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





def 	__a():



    '''simple docstring'''





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

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

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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


