



'''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       __UpperCamelCase	(       UpperCAmelCase					):  # picklable for multiprocessing
 return x.sum()
def       __UpperCamelCase	(       UpperCAmelCase					):  # picklable for multiprocessing
 return i + 1



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   4_2
       SCREAMING_SNAKE_CASE 		=   4_2



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Optional[int]						= {}
        lowercase__    :   List[str]						= []
        lowercase__    :   List[str]						= 1
        lowercase__    :   Any						= [1, 2]
        lowercase__    :   int						= {'''a''': 1, '''b''': 2}
        lowercase__    :   Any						= {'''a''': [1, 2], '''b''': [3, 4]}
        lowercase__    :   Any						= {'''a''': {'''1''': 1}, '''b''': 2}
        lowercase__    :   Optional[Any]						= {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        lowercase__    :   List[str]						= {}
        lowercase__    :   Union[str, Any]						= []
        lowercase__    :   List[Any]						= 2
        lowercase__    :   Dict						= [2, 3]
        lowercase__    :   Tuple						= {'''a''': 2, '''b''': 3}
        lowercase__    :   str						= {'''a''': [2, 3], '''b''': [4, 5]}
        lowercase__    :   Any						= {'''a''': {'''1''': 2}, '''b''': 3}
        lowercase__    :   Optional[int]						= {'''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_			)

        lowercase__    :   List[str]						= 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_			)

        lowercase__    :   List[str]						= {'''a''': np.eye(2			), '''b''': np.zeros(3			), '''c''': np.ones(2			)}
        lowercase__    :   str						= {'''a''': 2, '''b''': 0, '''c''': 2}
        lowercase__    :   List[Any]						= {
            '''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 _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   str						= {'''a''': 1, '''b''': 2}
        lowercase__    :   Tuple						= {'''a''': 3, '''b''': 4}
        lowercase__    :   Tuple						= {'''a''': 5, '''b''': 6}
        lowercase__    :   List[str]						= sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]			)
        self.assertEqual(sorted(zip_dict(lowerCamelCase_     ,       lowerCamelCase_     ,       lowerCamelCase_			)			)     ,       lowerCamelCase_			)


       def _lowerCAmelCase(						self			)  ->  int:



        class       UpperCAmelCase  :





               '''simple docstring'''



               SCREAMING_SNAKE_CASE 		=   "bar"


        lowercase__    :   Tuple						= 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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):





 with patch('''datasets.utils.py_utils._single_map_nested'''					) as mock_single_map_nested, patch(
     '''datasets.parallel.parallel.Pool'''					) as mock_multiprocessing_pool:
  lowercase__    :   List[str]						= {F"""{i}""": i for i in range(UpperCAmelCase					)}
  lowercase__    :   int						= 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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       @require_tf
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        import tensorflow as tf
        from tensorflow.keras import layers

        lowercase__    :   Any						= layers.Dense(2			)

        def gen_random_output():
         lowercase__    :   Optional[int]						= tf.random.uniform((1, 3)			)
         return model(lowerCamelCase_			).numpy()

        with temp_seed(42     ,       set_tensorflow=lowerCamelCase_			):
         lowercase__    :   Tuple						= gen_random_output()
        with temp_seed(42     ,       set_tensorflow=lowerCamelCase_			):
         lowercase__    :   int						= gen_random_output()
        lowercase__    :   List[str]						= gen_random_output()

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

       @require_torch
       def _lowerCAmelCase(						self			)  ->  str:
        import torch

        def gen_random_output():
         lowercase__    :   Optional[Any]						= torch.nn.Linear(3     ,       2			)
         lowercase__    :   str						= torch.rand(1     ,       3			)
         return model(lowerCamelCase_			).detach().numpy()

        with temp_seed(42     ,       set_pytorch=lowerCamelCase_			):
         lowercase__    :   Any						= gen_random_output()
        with temp_seed(42     ,       set_pytorch=lowerCamelCase_			):
         lowercase__    :   Union[str, Any]						= gen_random_output()
        lowercase__    :   Union[str, Any]						= gen_random_output()

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


       def _lowerCAmelCase(						self			)  ->  Optional[Any]:

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

        with temp_seed(42			):
         lowercase__    :   Tuple						= gen_random_output()
        with temp_seed(42			):
         lowercase__    :   Optional[int]						= gen_random_output()
        lowercase__    :   Union[str, Any]						= gen_random_output()

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


@pytest.mark.parametrize('''input_data'''		,					[{}]					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= 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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Tuple						= NestedDataStructure(UpperCAmelCase					).flatten()
 assert output == expected_output
def       __UpperCamelCase	(       ):
 lowercase__    :   Optional[int]						= A(x=1		,					y='''foobar'''					)
 lowercase__    :   Optional[Any]						= {'''x''': 1, '''y''': '''foobar'''}
 assert asdict(UpperCAmelCase					) == expected_output

 lowercase__    :   int						= {'''a''': {'''b''': A(x=10		,					y='''foo'''					)}, '''c''': [A(x=20		,					y='''bar'''					)]}
 lowercase__    :   Optional[Any]						= {'''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       __UpperCamelCase	(       UpperCAmelCase					):
 return text.split()
def       __UpperCamelCase	(       UpperCAmelCase					):
 yield (time.time(), content)
 time.sleep(2					)
 yield (time.time(), content)





def       __UpperCamelCase	(       ):





 with Pool(2					) as pool:
  lowercase__    :   List[str]						= 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:
  lowercase__    :   List[str]						= 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:
  lowercase__    :   Dict						= []
  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


'''simple docstring'''

import re

from filelock import FileLock


try:
  import nltk

  __a:			Tuple	       =       True
except (ImportError, ModuleNotFoundError):
  __a:			List[Any]	       =       False

if NLTK_AVAILABLE:
  with FileLock(""".lock""") as lock:
    nltk.download("""punkt""", quiet=True)
def       __UpperCamelCase	(       UpperCAmelCase					):
 re.sub('''<n>'''		,					''''''		,					UpperCAmelCase					)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(UpperCAmelCase					)					)

