






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






def 							A     (       a_					)     ->						Tuple:  # picklable for multiprocessing
					return i + 1




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      int
				UpperCamelCase__ :      str




class 				__A     (			a ):





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		={}
									__UpperCamelCase      :					Union[str, Any]       		=[]
									__UpperCamelCase      :					List[str]       		=1
									__UpperCamelCase      :					List[Any]       		=[1, 2]
									__UpperCamelCase      :					Dict       		={'a': 1, 'b': 2}
									__UpperCamelCase      :					Union[str, Any]       		={'a': [1, 2], 'b': [3, 4]}
									__UpperCamelCase      :					Tuple       		={'a': {'1': 1}, 'b': 2}
									__UpperCamelCase      :					Union[str, Any]       		={'a': 1, 'b': 2, 'c': 3, 'd': 4}
									__UpperCamelCase      :					Dict       		={}
									__UpperCamelCase      :					Optional[Any]       		=[]
									__UpperCamelCase      :					str       		=2
									__UpperCamelCase      :					str       		=[2, 3]
									__UpperCamelCase      :					Any       		={'a': 2, 'b': 3}
									__UpperCamelCase      :					Any       		={'a': [2, 3], 'b': [4, 5]}
									__UpperCamelCase      :					List[str]       		={'a': {'1': 2}, 'b': 3}
									__UpperCamelCase      :					str       		={'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__    )

									__UpperCamelCase      :					Tuple       		=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__    )

									__UpperCamelCase      :					Tuple       		={'a': np.eye(2    ), 'b': np.zeros(3    ), 'c': np.ones(2    )}
									__UpperCamelCase      :					List[Any]       		={'a': 2, 'b': 0, 'c': 2}
									__UpperCamelCase      :					Tuple       		={
									    '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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		={'a': 1, 'b': 2}
									__UpperCamelCase      :					Any       		={'a': 3, 'b': 4}
									__UpperCamelCase      :					Union[str, Any]       		={'a': 5, 'b': 6}
									__UpperCamelCase      :					Dict       		=sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]    )
									self.assertEqual(sorted(zip_dict(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    )    )							,  lowerCamelCase__    )







				def       __lowercase							( self    ):




									"""simple docstring"""

									class 				__A     :





													"""simple docstring"""







													UpperCamelCase__ :      int			 ="""bar"""
									__UpperCamelCase      :					List[Any]       		=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     (       a_							,a_							,a_					)     ->						List[Any]:
					with patch('datasets.utils.py_utils._single_map_nested'					) as mock_single_map_nested, patch(
					    'datasets.parallel.parallel.Pool'					) as mock_multiprocessing_pool:
										__UpperCamelCase      :					Union[str, Any]       		={F'{i}': i for i in range(a_					)}
										__UpperCamelCase      :					List[str]       		=map_nested(lambda a_					: x + 10							,a_							,num_proc=a_							,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 				__A     (			a ):





				"""simple docstring"""







				@require_tf
				def       __lowercase							( self    ):




									"""simple docstring"""

									import tensorflow as tf
									from tensorflow.keras import layers

									__UpperCamelCase      :					Dict       		=layers.Dense(2    )

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

									with temp_seed(42							,  set_tensorflow=lowerCamelCase__    ):
														__UpperCamelCase      :					Optional[int]       		=gen_random_output()
									with temp_seed(42							,  set_tensorflow=lowerCamelCase__    ):
														__UpperCamelCase      :					Tuple       		=gen_random_output()
									__UpperCamelCase      :					List[str]       		=gen_random_output()

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


				@require_torch
				def       __lowercase							( self    ):




									"""simple docstring"""

									import torch

									def gen_random_output():
														__UpperCamelCase      :					Tuple       		=torch.nn.Linear(3							,  2    )
														__UpperCamelCase      :					List[str]       		=torch.rand(1							,  3    )
														return model(lowerCamelCase__    ).detach().numpy()

									with temp_seed(42							,  set_pytorch=lowerCamelCase__    ):
														__UpperCamelCase      :					Tuple       		=gen_random_output()
									with temp_seed(42							,  set_pytorch=lowerCamelCase__    ):
														__UpperCamelCase      :					int       		=gen_random_output()
									__UpperCamelCase      :					Union[str, Any]       		=gen_random_output()

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







				def       __lowercase							( self    ):




									"""simple docstring"""

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

									with temp_seed(42    ):
														__UpperCamelCase      :					List[str]       		=gen_random_output()
									with temp_seed(42    ):
														__UpperCamelCase      :					List[Any]       		=gen_random_output()
									__UpperCamelCase      :					List[str]       		=gen_random_output()

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






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






def 							A     (       )     ->						int:
					__UpperCamelCase      :					int       		=A(x=1							,y='foobar'					)
					__UpperCamelCase      :					Dict       		={'x': 1, 'y': 'foobar'}
					assert asdict(a_					) == expected_output

					__UpperCamelCase      :					Tuple       		={'a': {'b': A(x=10							,y='foo'					)}, 'c': [A(x=20							,y='bar'					)]}
					__UpperCamelCase      :					Optional[Any]       		={'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
					assert asdict(a_					) == expected_output

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






def 							A     (       a_					)     ->						int:
					return text.split()






def 							A     (       a_					)     ->						Optional[Any]:
					yield (time.time(), content)
					time.sleep(2					)
					yield (time.time(), content)






def 							A     (       )     ->						List[Any]:
					with Pool(2					) as pool:
										__UpperCamelCase      :					Optional[Any]       		=list(iflatmap_unordered(a_							,_split_text							,kwargs_iterable=[{'text': 'hello there'}] * 10					)					)
										assert out.count('hello'					) == 10
										assert out.count('there'					) == 10
										assert len(a_					) == 20

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

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



from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




