






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					(			UpperCAmelCase__       ):





							"""simple docstring"""

							def    __A			(	self: List[Any] )     ->					Tuple:
												_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(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__ )

												_A										=			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__ )

												_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(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 __A : x + 1		,  lowerCamelCase__		,  num_proc=lowerCamelCase__ )

							def    __A			(	self: str )     ->					Optional[int]:
												_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(lowerCamelCase__		,  lowerCamelCase__		,  lowerCamelCase__ ) )		,  lowerCamelCase__ )



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





												class     SCREAMING_SNAKE_CASE					:





																			"""simple docstring"""

																			A_    	=       'bar'



												_A										=			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       (  _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:
										_A										=			{f"""{i}""": i for i in range(_lowercase			)}
										_A										=			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					(			UpperCAmelCase__       ):





							"""simple docstring"""

							@require_tf
							def    __A			(	self: str )     ->					Union[str, Any]:
												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(lowerCamelCase__ ).numpy()

												with temp_seed(42		,  set_tensorflow=lowerCamelCase__ ):
																	_A										=			gen_random_output()
												with temp_seed(42		,  set_tensorflow=lowerCamelCase__ ):
																	_A										=			gen_random_output()
												_A										=			gen_random_output()

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

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

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

												with temp_seed(42		,  set_pytorch=lowerCamelCase__ ):
																	_A										=			gen_random_output()
												with temp_seed(42		,  set_pytorch=lowerCamelCase__ ):
																	_A										=			gen_random_output()
												_A										=			gen_random_output()

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



							def    __A			(	self: Optional[Any] )     ->					int:
												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(lowerCamelCase__		,  lowerCamelCase__ )
												self.assertGreater(np.abs(outa - outa ).sum()		,  0 )




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

					'''simple docstring'''







					_A										=			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'''







					_A										=			NestedDataStructure(_lowercase			).flatten()
					assert output == expected_output




def   __A       (  ):

					'''simple docstring'''







					_A										=			A(x=1       ,      y='''foobar'''			)
					_A										=			{"x": 1, "y": "foobar"}
					assert asdict(_lowercase			) == 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(_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:
										_A										=			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:
										_A										=			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:
										_A										=			[]
										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




def 		snake_case__	(		lowercase							,				lowercase					):

   if density <= 0:
      raise ValueError("Impossible fluid density"					)
   if bulk_modulus <= 0:
      raise ValueError("Impossible bulk modulus"					)

   return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()