





import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

_lowerCamelCase			       =      {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

_lowerCamelCase			       =      {
    'jukebox': 512,
}







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      VOCAB_FILES_NAMES
      UpperCAmelCase__						=      PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__						=      PRETRAINED_LYRIC_TOKENS_SIZES
      UpperCAmelCase__						=      ["""input_ids""", """attention_mask"""]


      def __init__(		self      ,							a__      ,							a__      ,							a__      ,							a__=["v3", "v2", "v2"]      ,							a__=512      ,							a__=5      ,							a__="<|endoftext|>"      ,							**a__      ,							):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		AddedToken(a__      ,							lstrip=a__      ,							rstrip=a__) if isinstance(a__      ,							a__) else unk_token
             super().__init__(
                 unk_token=a__      ,							n_genres=a__      ,							version=a__      ,							max_n_lyric_tokens=a__      ,							**a__      ,							)
             _lowerCamelCase							:     int	=		version
             _lowerCamelCase							:     Any	=		max_n_lyric_tokens
             _lowerCamelCase							:     Tuple	=		n_genres

             with open(a__      ,							encoding='''utf-8''') as vocab_handle:
                    _lowerCamelCase							:     Union[str, Any]	=		json.load(a__)

             with open(a__      ,							encoding='''utf-8''') as vocab_handle:
                    _lowerCamelCase							:     Union[str, Any]	=		json.load(a__)

             with open(a__      ,							encoding='''utf-8''') as vocab_handle:
                    _lowerCamelCase							:     Dict	=		json.load(a__)

             _lowerCamelCase							:     Optional[int]	=		r'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
             # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
             if len(self.lyrics_encoder) == 79:
                    _lowerCamelCase							:     List[str]	=		oov.replace(r'''\-\''''      ,							r'''\-+\'''')

             _lowerCamelCase							:     Dict	=		regex.compile(a__)
             _lowerCamelCase							:     Union[str, Any]	=		{v: k for k, v in self.artists_encoder.items()}
             _lowerCamelCase							:     Dict	=		{v: k for k, v in self.genres_encoder.items()}
             _lowerCamelCase							:     Dict	=		{v: k for k, v in self.lyrics_encoder.items()}


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)


      def 				__snake_case			(		self):



             """simple docstring"""





             return dict(self.artists_encoder      ,							self.genres_encoder      ,							self.lyrics_encoder)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		[self.artists_encoder.get(a__      ,							0) for artist in list_artists]
             for genres in range(len(a__)):
                    _lowerCamelCase							:     List[str]	=		[self.genres_encoder.get(a__      ,							0) for genre in list_genres[genres]]
                    _lowerCamelCase							:     str	=		list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

             _lowerCamelCase							:     int	=		[[self.lyrics_encoder.get(a__      ,							0) for character in list_lyrics[0]], [], []]
             return artists_id, list_genres, lyric_ids


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             return list(a__)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							**a__):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Any	=		self.prepare_for_tokenization(a__      ,							a__      ,							a__)
             _lowerCamelCase							:     Tuple	=		self._tokenize(a__)
             return artist, genre, lyrics


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							a__ = False):



             """simple docstring"""





             for idx in range(len(self.version)):
                    if self.version[idx] == "v3":
                           _lowerCamelCase							:     Tuple	=		artists[idx].lower()
                           _lowerCamelCase							:     Optional[Any]	=		[genres[idx].lower()]
                    else:
                           _lowerCamelCase							:     Tuple	=		self._normalize(artists[idx]) + '''.v2'''
                           _lowerCamelCase							:     List[Any]	=		[
                               self._normalize(a__) + '''.v2''' for genre in genres[idx].split('''_''')
                           ]  # split is for the full dictionary with combined genres

             if self.version[0] == "v2":
                    _lowerCamelCase							:     Any	=		regex.compile(r'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+''')
                    _lowerCamelCase							:     Optional[Any]	=		'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
                    _lowerCamelCase							:     Union[str, Any]	=		{vocab[index]: index + 1 for index in range(len(a__))}
                    _lowerCamelCase							:     Optional[int]	=		0
                    _lowerCamelCase							:     List[Any]	=		len(a__) + 1
                    _lowerCamelCase							:     List[Any]	=		self.vocab
                    _lowerCamelCase							:     Tuple	=		{v: k for k, v in self.vocab.items()}
                    _lowerCamelCase							:     Any	=		''''''
             else:
                    _lowerCamelCase							:     Union[str, Any]	=		regex.compile(r'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+''')

             _lowerCamelCase							:     Dict	=		self._run_strip_accents(a__)
             _lowerCamelCase							:     int	=		lyrics.replace('''\\'''      ,							'''\n''')
             _lowerCamelCase							:     str	=		self.out_of_vocab.sub(''''''      ,							a__), [], []
             return artists, genres, lyrics


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		unicodedata.normalize('''NFD'''      ,							a__)
             _lowerCamelCase							:     List[Any]	=		[]
             for char in text:
                    _lowerCamelCase							:     Any	=		unicodedata.category(a__)
                    if cat == "Mn":
                           continue
                    output.append(a__)
             return "".join(a__)


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		(
                 [chr(a__) for i in range(ord('''a''')      ,							ord('''z''') + 1)]
                 + [chr(a__) for i in range(ord('''A''')      ,							ord('''Z''') + 1)]
                 + [chr(a__) for i in range(ord('''0''')      ,							ord('''9''') + 1)]
                 + ['''.''']
             )
             _lowerCamelCase							:     int	=		frozenset(a__)
             _lowerCamelCase							:     Optional[int]	=		re.compile(r'''_+''')
             _lowerCamelCase							:     int	=		''''''.join([c if c in accepted else '''_''' for c in text.lower()])
             _lowerCamelCase							:     Optional[int]	=		pattern.sub('''_'''      ,							a__).strip('''_''')
             return text


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             return " ".join(a__)


      def 				__snake_case			(		self      ,							a__      ,							a__ = None      ,							a__ = False):



             """simple docstring"""





             if not isinstance(a__      ,							a__):
                    _lowerCamelCase							:     List[Any]	=		TensorType(a__)

             # Get a function reference for the correct framework
             if tensor_type == TensorType.TENSORFLOW:
                    if not is_tf_available():
                           raise ImportError(
                               '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.''')
                    import tensorflow as tf

                    _lowerCamelCase							:     Dict	=		tf.constant
                    _lowerCamelCase							:     List[Any]	=		tf.is_tensor
             elif tensor_type == TensorType.PYTORCH:
                    if not is_torch_available():
                           raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.''')
                    import torch

                    _lowerCamelCase							:     List[str]	=		torch.tensor
                    _lowerCamelCase							:     int	=		torch.is_tensor
             elif tensor_type == TensorType.JAX:
                    if not is_flax_available():
                           raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.''')
                    import jax.numpy as jnp  # noqa: F811

                    _lowerCamelCase							:     int	=		jnp.array
                    _lowerCamelCase							:     Tuple	=		_is_jax
             else:
                    _lowerCamelCase							:     List[str]	=		np.asarray
                    _lowerCamelCase							:     Dict	=		_is_numpy

             # Do the tensor conversion in batch

             try:
                    if prepend_batch_axis:
                           _lowerCamelCase							:     Any	=		[inputs]

                    if not is_tensor(a__):
                           _lowerCamelCase							:     int	=		as_tensor(a__)
             except:  # noqa E722
                    raise ValueError(
                        '''Unable to create tensor, you should probably activate truncation and/or padding '''
                        '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.''')

             return inputs


      def __call__(		self      ,							a__      ,							a__      ,							a__=""      ,							a__="pt"):



             """simple docstring"""





             _lowerCamelCase							:     int	=		[0, 0, 0]
             _lowerCamelCase							:     Union[str, Any]	=		[artist] * len(self.version)
             _lowerCamelCase							:     Tuple	=		[genres] * len(self.version)

             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.tokenize(a__      ,							a__      ,							a__)
             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		self._convert_token_to_id(a__      ,							a__      ,							a__)

             _lowerCamelCase							:     Union[str, Any]	=		[-INFINITY] * len(full_tokens[-1])
             _lowerCamelCase							:     List[str]	=		[
                 self.convert_to_tensors(
                     [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]      ,							tensor_type=a__)
                 for i in range(len(self.version))
             ]
             return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks})


      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             if not os.path.isdir(a__):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
                    return

             _lowerCamelCase							:     Dict	=		os.path.join(
                 a__      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file'''])
             with open(a__      ,							'''w'''      ,							encoding='''utf-8''') as f:
                    f.write(json.dumps(self.artists_encoder      ,							ensure_ascii=a__))

             _lowerCamelCase							:     List[Any]	=		os.path.join(
                 a__      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file'''])
             with open(a__      ,							'''w'''      ,							encoding='''utf-8''') as f:
                    f.write(json.dumps(self.genres_encoder      ,							ensure_ascii=a__))

             _lowerCamelCase							:     List[str]	=		os.path.join(
                 a__      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file'''])
             with open(a__      ,							'''w'''      ,							encoding='''utf-8''') as f:
                    f.write(json.dumps(self.lyrics_encoder      ,							ensure_ascii=a__))

             return (artists_file, genres_file, lyrics_file)



      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.artists_decoder.get(a__)
             _lowerCamelCase							:     Tuple	=		[self.genres_decoder.get(a__) for genre in genres_index]
             _lowerCamelCase							:     Union[str, Any]	=		[self.lyrics_decoder.get(a__) for character in lyric_index]
             return artist, genres, lyrics








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(      lowercase_      ):  # picklable for multiprocessing
       return x.sum()




def    __UpperCAmelCase(      lowercase_      ):  # picklable for multiprocessing
       return i + 1







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		{}
             _lowerCamelCase							:     Dict	=		[]
             _lowerCamelCase							:     Optional[Any]	=		1
             _lowerCamelCase							:     Optional[int]	=		[1, 2]
             _lowerCamelCase							:     str	=		{'''a''': 1, '''b''': 2}
             _lowerCamelCase							:     Dict	=		{'''a''': [1, 2], '''b''': [3, 4]}
             _lowerCamelCase							:     Any	=		{'''a''': {'''1''': 1}, '''b''': 2}
             _lowerCamelCase							:     Optional[Any]	=		{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
             _lowerCamelCase							:     str	=		{}
             _lowerCamelCase							:     int	=		[]
             _lowerCamelCase							:     str	=		2
             _lowerCamelCase							:     int	=		[2, 3]
             _lowerCamelCase							:     str	=		{'''a''': 2, '''b''': 3}
             _lowerCamelCase							:     Tuple	=		{'''a''': [2, 3], '''b''': [4, 5]}
             _lowerCamelCase							:     List[str]	=		{'''a''': {'''1''': 2}, '''b''': 3}
             _lowerCamelCase							:     str	=		{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__)      ,							a__)

             _lowerCamelCase							:     Dict	=		2
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)
             self.assertEqual(map_nested(a__      ,							a__      ,							num_proc=a__)      ,							a__)

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		{'''a''': 1, '''b''': 2}
             _lowerCamelCase							:     Optional[int]	=		{'''a''': 3, '''b''': 4}
             _lowerCamelCase							:     int	=		{'''a''': 5, '''b''': 6}
             _lowerCamelCase							:     Optional[int]	=		sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
             self.assertEqual(sorted(zip_dict(a__      ,							a__      ,							a__))      ,							a__)



      def 				__snake_case			(		self):



             """simple docstring"""





             class 			__A       :




                   """simple docstring"""







                   UpperCAmelCase__						=      """bar"""
             _lowerCamelCase							:     Any	=		Foo()
             self.assertEqual(foo.my_attr      ,							'''bar''')
             with temporary_assignment(a__      ,							'''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(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       with patch('''datasets.utils.py_utils._single_map_nested'''      ) as mock_single_map_nested, patch(
           '''datasets.parallel.parallel.Pool'''      ) as mock_multiprocessing_pool:
              _lowerCamelCase							:     Union[str, Any]	=		{F"""{i}""": i for i in range(lowercase_      )}
              _lowerCamelCase							:     List[str]	=		map_nested(lambda lowercase_      : x + 10 ,    lowercase_ ,    num_proc=lowercase_ ,    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       (      lowerCamelCase__  ):




      """simple docstring"""







      @require_tf
      def 				__snake_case			(		self):



             """simple docstring"""





             import tensorflow as tf
             from tensorflow.keras import layers

             _lowerCamelCase							:     int	=		layers.Dense(2)

             def gen_random_output():
                    _lowerCamelCase							:     Union[str, Any]	=		tf.random.uniform((1, 3))
                    return model(a__).numpy()

             with temp_seed(42      ,							set_tensorflow=a__):
                    _lowerCamelCase							:     List[str]	=		gen_random_output()
             with temp_seed(42      ,							set_tensorflow=a__):
                    _lowerCamelCase							:     Any	=		gen_random_output()
             _lowerCamelCase							:     str	=		gen_random_output()

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


      @require_torch
      def 				__snake_case			(		self):



             """simple docstring"""





             import torch

             def gen_random_output():
                    _lowerCamelCase							:     Union[str, Any]	=		torch.nn.Linear(3      ,							2)
                    _lowerCamelCase							:     Dict	=		torch.rand(1      ,							3)
                    return model(a__).detach().numpy()

             with temp_seed(42      ,							set_pytorch=a__):
                    _lowerCamelCase							:     Any	=		gen_random_output()
             with temp_seed(42      ,							set_pytorch=a__):
                    _lowerCamelCase							:     Optional[int]	=		gen_random_output()
             _lowerCamelCase							:     Union[str, Any]	=		gen_random_output()

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



      def 				__snake_case			(		self):



             """simple docstring"""





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

             with temp_seed(42):
                    _lowerCamelCase							:     Union[str, Any]	=		gen_random_output()
             with temp_seed(42):
                    _lowerCamelCase							:     List[str]	=		gen_random_output()
             _lowerCamelCase							:     str	=		gen_random_output()

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




@pytest.mark.parametrize('''input_data''' ,    [{}]      )
def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     List[Any]	=		NestedDataStructure(lowercase_      ).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(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		NestedDataStructure(lowercase_      ).flatten()
       assert output == expected_output




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Any	=		A(x=1 ,    y='''foobar'''      )
       _lowerCamelCase							:     Union[str, Any]	=		{'''x''': 1, '''y''': '''foobar'''}
       assert asdict(lowercase_      ) == expected_output

       _lowerCamelCase							:     Optional[int]	=		{'''a''': {'''b''': A(x=10 ,    y='''foo'''      )}, '''c''': [A(x=20 ,    y='''bar'''      )]}
       _lowerCamelCase							:     Union[str, Any]	=		{'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
       assert asdict(lowercase_      ) == expected_output

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




def    __UpperCAmelCase(      lowercase_      ):
       return text.split()




def    __UpperCAmelCase(      lowercase_      ):
       yield (time.time(), content)
       time.sleep(2      )
       yield (time.time(), content)




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

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

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

