import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import WhisperFeatureExtractor

if is_torch_available():
    import torch

__lowercase											=      random.Random()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=1.0						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    if rng is None:
        __UpperCamelCase  :Optional[Any]    						=	global_rng

    __UpperCamelCase  :Dict    						=	[]
    for batch_idx in range(shape[0]			):
        values.append([]			)
        for _ in range(shape[1]			):
            values[-1].append(rng.random() * scale			)

    return values


@require_torch
@require_torchaudio
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=7				,				__lowercase=400				,				__lowercase=2_000				,				__lowercase=10				,				__lowercase=160				,				__lowercase=8				,				__lowercase=0.0				,				__lowercase=4_000				,				__lowercase=False				,				__lowercase=True				,				) ->			Dict:
        __UpperCamelCase  :List[Any]    						=	parent
        __UpperCamelCase  :int    						=	batch_size
        __UpperCamelCase  :int    						=	min_seq_length
        __UpperCamelCase  :Any    						=	max_seq_length
        __UpperCamelCase  :int    						=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        __UpperCamelCase  :int    						=	padding_value
        __UpperCamelCase  :Dict    						=	sampling_rate
        __UpperCamelCase  :Optional[int]    						=	return_attention_mask
        __UpperCamelCase  :Optional[int]    						=	do_normalize
        __UpperCamelCase  :List[str]    						=	feature_size
        __UpperCamelCase  :str    						=	chunk_length
        __UpperCamelCase  :Any    						=	hop_length







    def     UpperCamelCase__     (			self) ->			str:
        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







    def     UpperCamelCase__     (			self				,				__lowercase=False				,				__lowercase=False) ->			Optional[int]:
        def _flatten(__lowercase):
            return list(itertools.chain(*__lowercase))

        if equal_length:
            __UpperCamelCase  :Optional[Any]    						=	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
        else:
            # make sure that inputs increase in size
            __UpperCamelCase  :List[Any]    						=	[
                floats_list((x, self.feature_size))
                for x in range(self.min_seq_length				,				self.max_seq_length				,				self.seq_length_diff)
            ]
        if numpify:
            __UpperCamelCase  :List[Any]    						=	[np.asarray(__lowercase) for x in speech_inputs]
        return speech_inputs


@require_torch
@require_torchaudio
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   WhisperFeatureExtractor if is_speech_available() else None







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Dict    						=	WhisperFeatureExtractionTester(self)







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase  :Optional[Any]    						=	feat_extract_first.save_pretrained(__lowercase)[0]
            check_json_file_has_correct_format(__lowercase)
            __UpperCamelCase  :List[Any]    						=	self.feature_extraction_class.from_pretrained(__lowercase)

        __UpperCamelCase  :Optional[int]    						=	feat_extract_first.to_dict()
        __UpperCamelCase  :List[str]    						=	feat_extract_second.to_dict()
        __UpperCamelCase  :int    						=	feat_extract_first.mel_filters
        __UpperCamelCase  :Optional[Any]    						=	feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase				,				__lowercase))
        self.assertEqual(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase  :Union[str, Any]    						=	os.path.join(__lowercase				,				'''feat_extract.json''')
            feat_extract_first.to_json_file(__lowercase)
            __UpperCamelCase  :Optional[Any]    						=	self.feature_extraction_class.from_json_file(__lowercase)

        __UpperCamelCase  :str    						=	feat_extract_first.to_dict()
        __UpperCamelCase  :List[Any]    						=	feat_extract_second.to_dict()
        __UpperCamelCase  :int    						=	feat_extract_first.mel_filters
        __UpperCamelCase  :List[Any]    						=	feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase				,				__lowercase))
        self.assertEqual(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        __UpperCamelCase  :Any    						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        __UpperCamelCase  :Tuple    						=	[floats_list((1, x))[0] for x in range(800				,				1_400				,				200)]
        __UpperCamelCase  :List[Any]    						=	[np.asarray(__lowercase) for speech_input in speech_inputs]

        # Test feature size
        __UpperCamelCase  :Dict    						=	feature_extractor(__lowercase				,				padding='''max_length'''				,				return_tensors='''np''').input_features
        self.assertTrue(input_features.ndim == 3)
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)

        # Test not batched input
        __UpperCamelCase  :Optional[Any]    						=	feature_extractor(speech_inputs[0]				,				return_tensors='''np''').input_features
        __UpperCamelCase  :Tuple    						=	feature_extractor(np_speech_inputs[0]				,				return_tensors='''np''').input_features
        self.assertTrue(np.allclose(__lowercase				,				__lowercase				,				atol=1E-3))

        # Test batched
        __UpperCamelCase  :Any    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        __UpperCamelCase  :Any    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase				,				__lowercase):
            self.assertTrue(np.allclose(__lowercase				,				__lowercase				,				atol=1E-3))

        # Test 2-D numpy arrays are batched.
        __UpperCamelCase  :Optional[Any]    						=	[floats_list((1, x))[0] for x in (800, 800, 800)]
        __UpperCamelCase  :str    						=	np.asarray(__lowercase)
        __UpperCamelCase  :Any    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        __UpperCamelCase  :Dict    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase				,				__lowercase):
            self.assertTrue(np.allclose(__lowercase				,				__lowercase				,				atol=1E-3))

        # Test truncation required
        __UpperCamelCase  :Dict    						=	[floats_list((1, x))[0] for x in range(200				,				(feature_extractor.n_samples + 500)				,				200)]
        __UpperCamelCase  :int    						=	[np.asarray(__lowercase) for speech_input in speech_inputs]

        __UpperCamelCase  :Optional[int]    						=	[x[: feature_extractor.n_samples] for x in speech_inputs]
        __UpperCamelCase  :Union[str, Any]    						=	[np.asarray(__lowercase) for speech_input in speech_inputs_truncated]

        __UpperCamelCase  :List[Any]    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        __UpperCamelCase  :int    						=	feature_extractor(__lowercase				,				return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase				,				__lowercase):
            self.assertTrue(np.allclose(__lowercase				,				__lowercase				,				atol=1E-3))







    def     UpperCamelCase__     (			self) ->			int:
        import torch

        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        __UpperCamelCase  :Tuple    						=	np.random.rand(100				,				32).astype(np.floataa)
        __UpperCamelCase  :List[str]    						=	np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
            __UpperCamelCase  :int    						=	feature_extractor.pad([{'''input_features''': inputs}]				,				return_tensors='''np''')
            self.assertTrue(np_processed.input_features.dtype == np.floataa)
            __UpperCamelCase  :Tuple    						=	feature_extractor.pad([{'''input_features''': inputs}]				,				return_tensors='''pt''')
            self.assertTrue(pt_processed.input_features.dtype == torch.floataa)







    def     UpperCamelCase__     (			self				,				__lowercase) ->			str:
        __UpperCamelCase  :Tuple    						=	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,				'''clean'''				,				split='''validation''')
        # automatic decoding with librispeech
        __UpperCamelCase  :Dict    						=	ds.sort('''id''').select(range(__lowercase))[:num_samples]['''audio''']

        return [x["array"] for x in speech_samples]







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        # fmt: off
        __UpperCamelCase  :str    						=	torch.tensor(
            [
                0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
            ])
        # fmt: on

        __UpperCamelCase  :List[Any]    						=	self._load_datasamples(1)
        __UpperCamelCase  :List[Any]    						=	WhisperFeatureExtractor()
        __UpperCamelCase  :List[str]    						=	feature_extractor(__lowercase				,				return_tensors='''pt''').input_features
        self.assertEqual(input_features.shape				,				(1, 80, 3_000))
        self.assertTrue(torch.allclose(input_features[0, 0, :30]				,				__lowercase				,				atol=1E-4))







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        __UpperCamelCase  :List[str]    						=	self._load_datasamples(1)[0]
        __UpperCamelCase  :Any    						=	((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        __UpperCamelCase  :Any    						=	feat_extract.zero_mean_unit_var_norm([audio]				,				attention_mask=__lowercase)[0]

        self.assertTrue(np.all(np.mean(__lowercase) < 1E-3))
        self.assertTrue(np.all(np.abs(np.var(__lowercase) - 1) < 1E-3))






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						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return x.sum()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return i + 1


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		int
    a__			:		str


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	{}
        __UpperCamelCase  :int    						=	[]
        __UpperCamelCase  :int    						=	1
        __UpperCamelCase  :int    						=	[1, 2]
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :Dict    						=	{'''a''': [1, 2], '''b''': [3, 4]}
        __UpperCamelCase  :Optional[int]    						=	{'''a''': {'''1''': 1}, '''b''': 2}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        __UpperCamelCase  :str    						=	{}
        __UpperCamelCase  :Any    						=	[]
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Optional[int]    						=	[2, 3]
        __UpperCamelCase  :List[Any]    						=	{'''a''': 2, '''b''': 3}
        __UpperCamelCase  :List[str]    						=	{'''a''': [2, 3], '''b''': [4, 5]}
        __UpperCamelCase  :int    						=	{'''a''': {'''1''': 2}, '''b''': 3}
        __UpperCamelCase  :Union[str, Any]    						=	{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	2
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	{'''a''': np.eye(2), '''b''': np.zeros(3), '''c''': np.ones(2)}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 2, '''b''': 0, '''c''': 2}
        __UpperCamelCase  :Optional[int]    						=	{
            '''a''': np.eye(2).astype(__lowercase),
            '''b''': np.zeros(3).astype(__lowercase),
            '''c''': np.ones(2).astype(__lowercase),
        }
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase)				,				__lowercase)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase).items()}				,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,				)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase				,				num_proc=__lowercase).items()}				,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,				)
        with self.assertRaises(__lowercase):  # can't pickle a local lambda
            map_nested(lambda __lowercase: x + 1				,				__lowercase				,				num_proc=__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :List[str]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :int    						=	{'''a''': 3, '''b''': 4}
        __UpperCamelCase  :List[str]    						=	{'''a''': 5, '''b''': 6}
        __UpperCamelCase  :List[Any]    						=	sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
        self.assertEqual(sorted(zip_dict(__lowercase				,				__lowercase				,				__lowercase))				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:


        class 			lowerCamelCase_  :






            '''simple docstring'''






            a__			:		int      			=   """bar"""
        __UpperCamelCase  :List[str]    						=	Foo()
        self.assertEqual(foo.my_attr				,				'''bar''')
        with temporary_assignment(__lowercase				,				'''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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''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:
        __UpperCamelCase  :Union[str, Any]    						=	{f"""{i}""": i for i in range(SCREAMING_SNAKE_CASE			)}
        __UpperCamelCase  :Dict    						=	map_nested(lambda SCREAMING_SNAKE_CASE			: x + 10						,    SCREAMING_SNAKE_CASE						,    num_proc=SCREAMING_SNAKE_CASE						,    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     UpperCamelCase__     (			self) ->			Optional[int]:
        import tensorflow as tf
        from tensorflow.keras import layers

        __UpperCamelCase  :Optional[Any]    						=	layers.Dense(2)

        def gen_random_output():
            __UpperCamelCase  :Optional[Any]    						=	tf.random.uniform((1, 3))
            return model(__lowercase).numpy()

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

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







    @require_torch
    def     UpperCamelCase__     (			self) ->			List[str]:
        import torch

        def gen_random_output():
            __UpperCamelCase  :List[Any]    						=	torch.nn.Linear(3				,				2)
            __UpperCamelCase  :Optional[Any]    						=	torch.rand(1				,				3)
            return model(__lowercase).detach().numpy()

        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :Union[str, Any]    						=	gen_random_output()
        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :List[str]    						=	gen_random_output()
        __UpperCamelCase  :Dict    						=	gen_random_output()

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







    def     UpperCamelCase__     (			self) ->			str:
        def gen_random_output():
            return np.random.rand(1				,				3)

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

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



@pytest.mark.parametrize('''input_data'''						,    [{}]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).flatten()
    assert output == expected_output



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	A(x=1						,    y='''foobar'''			)
    __UpperCamelCase  :Dict    						=	{'''x''': 1, '''y''': '''foobar'''}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

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

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return text.split()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     ):
    '''simple docstring'''







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

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

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





