



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       )    ->   List[str]:  # picklable for multiprocessing
			return x.sum()


def A    (							__UpperCamelCase       )    ->   Optional[Any]:  # picklable for multiprocessing
			return i + 1


@dataclass
class   __lowerCAmelCase  :
					"""simple docstring"""






					A__					:		int
					A__					:		str







class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								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(_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       )

								A__	  =     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       )

								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(_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]       ):






								"""simple docstring"""


								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(_snake_case					,       _snake_case					,       _snake_case       )       )					,       _snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								class   __lowerCAmelCase  :
													"""simple docstring"""






													A__					:		int      							=     "bar"







								A__	  =     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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[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__	  =     {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   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					@require_tf
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								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(_snake_case       ).numpy()

								with temp_seed(42					,       set_tensorflow=_snake_case       ):
											A__	  =     gen_random_output()
								with temp_seed(42					,       set_tensorflow=_snake_case       ):
											A__	  =     gen_random_output()
								A__	  =     gen_random_output()

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



					@require_torch
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								import torch

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

								with temp_seed(42					,       set_pytorch=_snake_case       ):
											A__	  =     gen_random_output()
								with temp_seed(42					,       set_pytorch=_snake_case       ):
											A__	  =     gen_random_output()
								A__	  =     gen_random_output()

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



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								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(_snake_case					,       _snake_case       )
								self.assertGreater(np.abs(outa - outa       ).sum()					,       0       )







@pytest.mark.parametrize('input_data'			,						[{}]       )
def A    (							__UpperCamelCase       )    ->   List[str]:
			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       )    ->   str:
			A__	  =     NestedDataStructure(__UpperCamelCase       ).flatten()
			assert output == expected_output


def A    (							)    ->   Tuple:
			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       )    ->   str:
			return text.split()


def A    (							__UpperCamelCase       )    ->   Tuple:
			yield (time.time(), content)
			time.sleep(2       )
			yield (time.time(), content)






def A    (							)    ->   Any:
			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











import mpmath  # for roots of unity
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__=None   ,				a__=None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		list(poly_a or [0])[:]
             _lowerCamelCase				:  Union[str, Any]						=		list(poly_b or [0])[:]

             # Remove leading zero coefficients
             while self.polyA[-1] == 0:
                    self.polyA.pop()
             _lowerCamelCase				:  Optional[int]						=		len(self.polyA)

             while self.polyB[-1] == 0:
                    self.polyB.pop()
             _lowerCamelCase				:  Any						=		len(self.polyB)

             # Add 0 to make lengths equal a power of 2
             _lowerCamelCase				:  Dict						=		int(
                 2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

             while len(self.polyA) < self.c_max_length:
                    self.polyA.append(0)
             while len(self.polyB) < self.c_max_length:
                    self.polyB.append(0)
             # A complex root used for the fourier transform
             _lowerCamelCase				:  int						=		complex(mpmath.root(x=1   ,				n=self.c_max_length   ,				k=1))

             # The product
             _lowerCamelCase				:  Optional[int]						=		self.__multiply()


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
             # Corner case
             if len(a__) <= 1:
                    return dft[0]
             #
             _lowerCamelCase				:  Optional[Any]						=		self.c_max_length // 2
             while next_ncol > 0:
                    _lowerCamelCase				:  Optional[Any]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  List[str]						=		self.root**next_ncol

                    # First half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Second half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Tuple						=		new_dft
                    _lowerCamelCase				:  Union[str, Any]						=		next_ncol // 2
             return dft[0]


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.__dft('''A''')
             _lowerCamelCase				:  Optional[Any]						=		self.__dft('''B''')
             _lowerCamelCase				:  List[str]						=		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
             del dft_a
             del dft_b

             # Corner Case
             if len(inverce_c[0]) <= 1:
                    return inverce_c[0]
             # Inverse DFT
             _lowerCamelCase				:  str						=		2
             while next_ncol <= self.c_max_length:
                    _lowerCamelCase				:  Optional[int]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  Dict						=		self.root ** (next_ncol // 2)
                    _lowerCamelCase				:  List[str]						=		1
                    # First half of next step
                    for j in range(self.c_max_length // next_ncol):
                           for i in range(next_ncol // 2):
                                  # Even positions
                                  new_inverse_c[i].append(
                                      (
                                          inverce_c[i][j]
                                          + inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / 2)
                                  # Odd positions
                                  new_inverse_c[i + next_ncol // 2].append(
                                      (
                                          inverce_c[i][j]
                                          - inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / (2 * current_root))
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Any						=		new_inverse_c
                    next_ncol *= 2
             # Unpack
             _lowerCamelCase				:  Optional[int]						=		[round(x[0].real   ,				8) + round(x[0].imag   ,				8) * 1j for x in inverce_c]

             # Remove leading 0's
             while inverce_c[-1] == 0:
                    inverce_c.pop()
             return inverce_c



      def __str__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		'''A = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]))
             _lowerCamelCase				:  List[str]						=		'''B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]))
             _lowerCamelCase				:  Tuple						=		'''A*B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.product))

             return F"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
       import doctest

       doctest.testmod()

