


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 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[str]:  # picklable for multiprocessing


						'''simple docstring'''






						return x.sum()





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Any:  # picklable for multiprocessing


						'''simple docstring'''






						return i + 1






@dataclass
class      UpperCamelCase:
					snake_case_		:					int
					snake_case_		:					str






class      UpperCamelCase(	_a ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		{}
											__snake_case				=		[]
											__snake_case				=		1
											__snake_case				=		[1, 2]
											__snake_case				=		{"a": 1, "b": 2}
											__snake_case				=		{"a": [1, 2], "b": [3, 4]}
											__snake_case				=		{"a": {"1": 1}, "b": 2}
											__snake_case				=		{"a": 1, "b": 2, "c": 3, "d": 4}
											__snake_case				=		{}
											__snake_case				=		[]
											__snake_case				=		2
											__snake_case				=		[2, 3]
											__snake_case				=		{"a": 2, "b": 3}
											__snake_case				=		{"a": [2, 3], "b": [4, 5]}
											__snake_case				=		{"a": {"1": 2}, "b": 3}
											__snake_case				=		{"a": 2, "b": 3, "c": 4, "d": 5}
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )

											__snake_case				=		2
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(map_nested(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     num_proc=SCREAMING_SNAKE_CASE    )  ,     SCREAMING_SNAKE_CASE    )

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


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




											'''simple docstring'''

											__snake_case				=		{"a": 1, "b": 2}
											__snake_case				=		{"a": 3, "b": 4}
											__snake_case				=		{"a": 5, "b": 6}
											__snake_case				=		sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]    )
											self.assertEqual(sorted(zip_dict(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )    )  ,     SCREAMING_SNAKE_CASE    )






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




											'''simple docstring'''

											class      UpperCamelCase:
																snake_case_		:					List[str]    					=						"""bar"""




											__snake_case				=		Foo()
											self.assertEqual(foo.my_attr  ,     "bar"    )
											with temporary_assignment(SCREAMING_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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''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:
												__snake_case				=		{F'''{i}''': i for i in range(_lowerCAmelCase			)}
												__snake_case				=		map_nested(lambda _lowerCAmelCase			: x + 10   ,						_lowerCAmelCase   ,						num_proc=_lowerCAmelCase   ,						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      UpperCamelCase(	_a ):


					@require_tf
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Tuple:




											'''simple docstring'''

											import tensorflow as tf
											from tensorflow.keras import layers

											__snake_case				=		layers.Dense(2    )

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

											with temp_seed(4_2  ,     set_tensorflow=SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		gen_random_output()
											with temp_seed(4_2  ,     set_tensorflow=SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		gen_random_output()
											__snake_case				=		gen_random_output()

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


					@require_torch
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Tuple:




											'''simple docstring'''

											import torch

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

											with temp_seed(4_2  ,     set_pytorch=SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		gen_random_output()
											with temp_seed(4_2  ,     set_pytorch=SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		gen_random_output()
											__snake_case				=		gen_random_output()

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






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




											'''simple docstring'''

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

											with temp_seed(4_2    ):
																	__snake_case				=		gen_random_output()
											with temp_seed(4_2    ):
																	__snake_case				=		gen_random_output()
											__snake_case				=		gen_random_output()

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





@pytest.mark.parametrize("input_data"   ,						[{}]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		NestedDataStructure(_lowerCAmelCase			).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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     )    ->						Optional[int]:


						'''simple docstring'''






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

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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						return text.split()





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     )    ->						List[Any]:


						'''simple docstring'''






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

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

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









from __future__ import annotations






class      UpperCamelCase:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		text, pattern
											__snake_case		,       __snake_case				=		len(SCREAMING_SNAKE_CASE    ), len(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if char == self.pattern[i]:
																							return i
											return -1


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if self.pattern[i] != self.text[current_pos + i]:
																							return current_pos + i
											return -1






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						list[int]:




											'''simple docstring'''

											__snake_case				=		[]
											for i in range(self.textLen - self.patLen + 1    ):
																	__snake_case				=		self.mismatch_in_text(SCREAMING_SNAKE_CASE    )
																	if mismatch_index == -1:
																							positions.append(SCREAMING_SNAKE_CASE    )
																	else:
																							__snake_case				=		self.match_in_pattern(self.text[mismatch_index]    )
																							__snake_case				=		(
																							    mismatch_index - match_index
																							)  # shifting index lgtm [py/multiple-definition]
											return positions




A				:       str      =  'ABAABA'
A				:       Optional[Any]      =  'AB'
A				:       Any      =  BoyerMooreSearch(text, pattern)
A				:       Tuple      =  bms.bad_character_heuristic()

if len(positions) == 0:
	print('No match found')
else:
	print('Pattern found in following positions: ')
	print(positions)





