
"""simple docstring"""




import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)


lowerCAmelCase__							=				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__							=				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase__							=				{'facebook/blenderbot_small-90M': 512}



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = set()
		SCREAMING_SNAKE_CASE_						 = word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)    )
				SCREAMING_SNAKE_CASE_						 = char

		SCREAMING_SNAKE_CASE_						 = set(__a    )
		return pairs





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      VOCAB_FILES_NAMES
							UpperCAmelCase__						     =      PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__						     =      ['''input_ids''', '''attention_mask''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_="__start__"       ,       SCREAMING_SNAKE_CASE_="__end__"       ,       SCREAMING_SNAKE_CASE_="__unk__"       ,       SCREAMING_SNAKE_CASE_="__null__"       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(unk_token=SCREAMING_SNAKE_CASE_       ,       bos_token=SCREAMING_SNAKE_CASE_       ,       eos_token=SCREAMING_SNAKE_CASE_       ,       pad_token=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

									with open(SCREAMING_SNAKE_CASE_       ,       encoding='''utf-8'''    ) as vocab_handle:
											SCREAMING_SNAKE_CASE_						 = json.load(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {v: k for k, v in self.encoder.items()}
									with open(SCREAMING_SNAKE_CASE_       ,       encoding='''utf-8'''    ) as merges_handle:
											SCREAMING_SNAKE_CASE_						 = merges_handle.read().split('''\n'''    )[1:-1]
									SCREAMING_SNAKE_CASE_						 = [tuple(merge.split()    ) for merge in merges]
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = {}

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return len(self.encoder    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return dict(self.encoder       ,       **self.added_tokens_encoder    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if token in self.cache:
											return self.cache[token]
									SCREAMING_SNAKE_CASE_						 = re.sub('''([.,!?()])'''       ,       R''' \1'''       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = re.sub('''(\')'''       ,       R''' \1 '''       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = re.sub(R'''\s{2,}'''       ,       ''' '''       ,       SCREAMING_SNAKE_CASE_    )
									if "\n" in token:
											SCREAMING_SNAKE_CASE_						 = token.replace('''\n'''       ,       ''' __newln__'''    )

									SCREAMING_SNAKE_CASE_						 = token.split(''' '''    )
									SCREAMING_SNAKE_CASE_						 = []
									for token in tokens:
											if not len(SCREAMING_SNAKE_CASE_    ):
													continue

											SCREAMING_SNAKE_CASE_						 = token.lower()
											SCREAMING_SNAKE_CASE_						 = tuple(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
											SCREAMING_SNAKE_CASE_						 = get_pairs(SCREAMING_SNAKE_CASE_    )

											if not pairs:
													words.append(SCREAMING_SNAKE_CASE_    )
													continue

											while True:
													SCREAMING_SNAKE_CASE_						 = min(SCREAMING_SNAKE_CASE_       ,       key=lambda SCREAMING_SNAKE_CASE_    : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_       ,       float('''inf'''    )    )    )
													if bigram not in self.bpe_ranks:
															break
													SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = bigram
													SCREAMING_SNAKE_CASE_						 = []
													SCREAMING_SNAKE_CASE_						 = 0

													while i < len(SCREAMING_SNAKE_CASE_    ):
															try:
																	SCREAMING_SNAKE_CASE_						 = word.index(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
																	new_word.extend(word[i:j]    )
																	SCREAMING_SNAKE_CASE_						 = j
															except ValueError:
																	new_word.extend(word[i:]    )
																	break

															if word[i] == first and i < len(SCREAMING_SNAKE_CASE_    ) - 1 and word[i + 1] == second:
																	new_word.append(first + second    )
																	i += 2
															else:
																	new_word.append(word[i]    )
																	i += 1
													SCREAMING_SNAKE_CASE_						 = tuple(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = new_word
													if len(SCREAMING_SNAKE_CASE_    ) == 1:
															break
													else:
															SCREAMING_SNAKE_CASE_						 = get_pairs(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = '''@@ '''.join(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = word[:-4]

											SCREAMING_SNAKE_CASE_						 = word
											words.append(SCREAMING_SNAKE_CASE_    )
									return " ".join(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []

									SCREAMING_SNAKE_CASE_						 = re.findall(R'''\S+\n?'''       ,       SCREAMING_SNAKE_CASE_    )

									for token in words:
											split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_    ).split(''' '''    )    )    )
									return split_tokens

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = token.lower()
									return self.encoder.get(SCREAMING_SNAKE_CASE_       ,       self.encoder.get(self.unk_token    )    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.decoder.get(SCREAMING_SNAKE_CASE_       ,       self.unk_token    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ''' '''.join(SCREAMING_SNAKE_CASE_    ).replace('''@@ '''       ,       ''''''    ).strip()
									return out_string







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None    ):

									"""simple docstring"""







									if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
											return
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    SCREAMING_SNAKE_CASE_       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    SCREAMING_SNAKE_CASE_       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

									with open(SCREAMING_SNAKE_CASE_       ,       '''w'''       ,       encoding='''utf-8'''    ) as f:
											f.write(json.dumps(self.encoder       ,       indent=2       ,       sort_keys=SCREAMING_SNAKE_CASE_       ,       ensure_ascii=SCREAMING_SNAKE_CASE_    ) + '''\n'''    )

									SCREAMING_SNAKE_CASE_						 = 0
									with open(SCREAMING_SNAKE_CASE_       ,       '''w'''       ,       encoding='''utf-8'''    ) as writer:
											writer.write('''#version: 0.2\n'''    )
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,       key=lambda SCREAMING_SNAKE_CASE_    : kv[1]    ):
													if index != token_index:
															logger.warning(
															    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
															    ''' Please check that the tokenizer is not corrupted!'''    )
															SCREAMING_SNAKE_CASE_						 = token_index
													writer.write(''' '''.join(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
													index += 1

									return vocab_file, merge_file

"""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        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return x.sum()



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return i + 1





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = [1, 2]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [1, 2], '''b''': [3, 4]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 1}, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = [2, 3]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [2, 3], '''b''': [4, 5]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 2}, '''b''': 3}
									SCREAMING_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_    )

									SCREAMING_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_    )

									SCREAMING_SNAKE_CASE_						 = {'''a''': np.eye(2    ), '''b''': np.zeros(3    ), '''c''': np.ones(2    )}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 0, '''c''': 2}
									SCREAMING_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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 3, '''b''': 4}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 5, '''b''': 6}
									SCREAMING_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 _lowercase					(self    ):

									"""simple docstring"""







									class  snake_case     :
																UpperCAmelCase__						     =      '''bar'''







									SCREAMING_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 ( __a,							__a,							__a    ):
		with patch('''datasets.utils.py_utils._single_map_nested'''    ) as mock_single_map_nested, patch(
		    '''datasets.parallel.parallel.Pool'''    ) as mock_multiprocessing_pool:
				SCREAMING_SNAKE_CASE_						 = {F'{i}': i for i in range(__a    )}
				SCREAMING_SNAKE_CASE_						 = 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  snake_case     (							__lowercase   ):

							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									import tensorflow as tf
									from tensorflow.keras import layers

									SCREAMING_SNAKE_CASE_						 = layers.Dense(2    )

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

									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_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 _lowercase					(self    ):

									"""simple docstring"""







									import torch

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

									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									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    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_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 ( __a    ):
		SCREAMING_SNAKE_CASE_						 = 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        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).flatten()
		assert output == expected_output



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = A(x=1,							y='''foobar'''    )
		SCREAMING_SNAKE_CASE_						 = {'''x''': 1, '''y''': '''foobar'''}
		assert asdict(__a    ) == expected_output

		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': A(x=10,							y='''foo'''    )}, '''c''': [A(x=20,							y='''bar'''    )]}
		SCREAMING_SNAKE_CASE_						 = {'''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        _lowerCamelCase ( __a    ):
		return text.split()



def        _lowerCamelCase ( __a    ):
		yield (time.time(), content)
		time.sleep(2    )
		yield (time.time(), content)



def        _lowerCamelCase ( ):
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = []
				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