"""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   )	->Dict:  # picklable for multiprocessing
  return x.sum()







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Dict:  # picklable for multiprocessing
  return i + 1




@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		42







class    __snake_case       (     __lowerCAmelCase				):



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





        '''simple docstring'''





        a__:     List[str]										=	{}
        a__:     Optional[int]										=	[]
        a__:     List[Any]										=	1
        a__:     str										=	[1, 2]
        a__:     Tuple										=	{'a': 1, 'b': 2}
        a__:     Dict										=	{'a': [1, 2], 'b': [3, 4]}
        a__:     List[str]										=	{'a': {'1': 1}, 'b': 2}
        a__:     Any										=	{'a': 1, 'b': 2, 'c': 3, 'd': 4}
        a__:     int										=	{}
        a__:     Union[str, Any]										=	[]
        a__:     Optional[int]										=	2
        a__:     Optional[int]										=	[2, 3]
        a__:     List[Any]										=	{'a': 2, 'b': 3}
        a__:     Optional[Any]										=	{'a': [2, 3], 'b': [4, 5]}
        a__:     List[str]										=	{'a': {'1': 2}, 'b': 3}
        a__:     Tuple										=	{'a': 2, 'b': 3, 'c': 4, 'd': 5}
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase)    ,			lowercase)

        a__:     Tuple										=	2
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)
        self.assertEqual(map_nested(lowercase    ,			lowercase    ,			num_proc=lowercase)    ,			lowercase)

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



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	{'a': 1, 'b': 2}
        a__:     Tuple										=	{'a': 3, 'b': 4}
        a__:     Optional[int]										=	{'a': 5, 'b': 6}
        a__:     Any										=	sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))])
        self.assertEqual(sorted(zip_dict(lowercase    ,			lowercase    ,			lowercase))    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        class    __snake_case       :
              a__        =		"""bar"""







        a__:     Optional[Any]										=	Foo()
        self.assertEqual(foo.my_attr    ,			'bar')
        with temporary_assignment(lowercase    ,			'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		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  with patch('datasets.utils.py_utils._single_map_nested'   ) as mock_single_map_nested, patch(
      'datasets.parallel.parallel.Pool'   ) as mock_multiprocessing_pool:
    a__:     int										=	{F'{i}': i for i in range(_SCREAMING_SNAKE_CASE   )}
    a__:     List[str]										=	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    __snake_case       (     __lowerCAmelCase				):



      @require_tf
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        import tensorflow as tf
        from tensorflow.keras import layers

        a__:     str										=	layers.Dense(2)

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

        with temp_seed(42    ,			set_tensorflow=lowercase):
          a__:     List[str]										=	gen_random_output()
        with temp_seed(42    ,			set_tensorflow=lowercase):
          a__:     Dict										=	gen_random_output()
        a__:     List[Any]										=	gen_random_output()

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



      @require_torch
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        import torch

        def gen_random_output():
          a__:     Optional[int]										=	torch.nn.Linear(3    ,			2)
          a__:     str										=	torch.rand(1    ,			3)
          return model(lowercase).detach().numpy()

        with temp_seed(42    ,			set_pytorch=lowercase):
          a__:     int										=	gen_random_output()
        with temp_seed(42    ,			set_pytorch=lowercase):
          a__:     Optional[int]										=	gen_random_output()
        a__:     int										=	gen_random_output()

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



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





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

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

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







@pytest.mark.parametrize('input_data'		,     [{}]   )
def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[Any]:
  a__:     Any										=	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		,     _SCREAMING_SNAKE_CASE   )	->int:
  a__:     List[Any]										=	NestedDataStructure(_SCREAMING_SNAKE_CASE   ).flatten()
  assert output == expected_output







def 						__a   (	)	->Dict:
  a__:     Dict										=	A(x=1		,     y='foobar'   )
  a__:     Optional[Any]										=	{'x': 1, 'y': 'foobar'}
  assert asdict(_SCREAMING_SNAKE_CASE   ) == expected_output

  a__:     List[Any]										=	{'a': {'b': A(x=10		,     y='foo'   )}, 'c': [A(x=20		,     y='bar'   )]}
  a__:     int										=	{'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   )	->Tuple:
  return text.split()







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Dict:
  yield (time.time(), content)
  time.sleep(2   )
  yield (time.time(), content)







def 						__a   (	)	->Dict:
  with Pool(2   ) as pool:
    a__:     str										=	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:
    a__:     Any										=	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:
    a__:     Tuple										=	[]
    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		,     _SCREAMING_SNAKE_CASE = False   )	->str:

  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     List[str]										=	F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	input_str.split('_'   )

  a__:     List[str]										=	0 if use_pascal else 1

  a__:     List[str]										=	words[start_index:]

  a__:     List[str]										=	[word[0].upper() + word[1:] for word in words_to_capitalize]

  a__:     List[str]										=	'' if use_pascal else words[0]

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


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

 testmod()


