





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

__lowerCamelCase :  str										=  logging.get_logger(__name__)


__lowerCamelCase :  List[str]										=  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCamelCase :  Optional[int]										=  {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__lowerCamelCase :  Any										=  {'facebook/blenderbot-3B': 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	A_       (  ):
 UpperCamelCase			:      List[Any] 			=     (
     list(range(ord("!"  )							,     ord("~"  ) + 1  )  ) + list(range(ord("¡"  )							,     ord("¬"  ) + 1  )  ) + list(range(ord("®"  )							,     ord("ÿ"  ) + 1  )  )
 )
 UpperCamelCase			:      List[Any] 			=     bs[:]
 UpperCamelCase			:      Optional[int] 			=     0
 for b in range(2**8  ):
  if b not in bs:
   bs.append(snake_case_  )
   cs.append(2**8 + n  )
   n += 1
 UpperCamelCase			:      Tuple 			=     [chr(snake_case_  ) for n in cs]
 return dict(zip(snake_case_							,     snake_case_  )  )



def 	A_       (  _lowerCAmelCase  ):
 UpperCamelCase			:      List[str] 			=     set()
 UpperCamelCase			:      Dict 			=     word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)  )
  UpperCamelCase			:      Dict 			=     char
 return pairs



class       A__     ( UpperCamelCase_    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:Dict							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     ['input_ids', 'attention_mask']





      def __init__(				self ,							A_ ,							A_ ,							A_="replace" ,							A_="<s>" ,							A_="</s>" ,							A_="</s>" ,							A_="<s>" ,							A_="<unk>" ,							A_="<pad>" ,							A_="<mask>" ,							A_=False ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else bos_token
       UpperCamelCase			:      List[str] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else eos_token
       UpperCamelCase			:      Optional[Any] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else sep_token
       UpperCamelCase			:      Tuple 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else cls_token
       UpperCamelCase			:      Optional[int] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else unk_token
       UpperCamelCase			:      Optional[int] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase			:      List[str] 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else mask_token

       super().__init__(
           errors=A_ ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							sep_token=A_ ,							cls_token=A_ ,							pad_token=A_ ,							mask_token=A_ ,							add_prefix_space=A_ ,							**A_ ,							)

       with open(A_ ,							encoding="utf-8"     ) as vocab_handle:
        UpperCamelCase			:      int 			=     json.load(A_     )
       UpperCamelCase			:      Tuple 			=     {v: k for k, v in self.encoder.items()}
       UpperCamelCase			:      Union[str, Any] 			=     errors  # how to handle errors in decoding
       UpperCamelCase			:      Tuple 			=     bytes_to_unicode()
       UpperCamelCase			:      int 			=     {v: k for k, v in self.byte_encoder.items()}
       with open(A_ ,							encoding="utf-8"     ) as merges_handle:
        UpperCamelCase			:      List[Any] 			=     merges_handle.read().split("\n"     )[1:-1]
       UpperCamelCase			:      Union[str, Any] 			=     [tuple(merge.split()     ) for merge in bpe_merges]
       UpperCamelCase			:      List[Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      int 			=     {}
       UpperCamelCase			:      Optional[int] 			=     add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCamelCase			:      Union[str, Any] 			=     re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"     )





      @property
      # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return len(self.encoder     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if token in self.cache:
        return self.cache[token]
       UpperCamelCase			:      Optional[Any] 			=     tuple(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     get_pairs(A_     )

       if not pairs:
        return token

       while True:
        UpperCamelCase			:      Union[str, Any] 			=     min(A_ ,							key=lambda A_     : self.bpe_ranks.get(A_ ,							float("inf"     )     )     )
        if bigram not in self.bpe_ranks:
         break
        UpperCamelCase			:      Optional[Any] 			=     bigram
        UpperCamelCase			:      List[str] 			=     []
        UpperCamelCase			:      Optional[int] 			=     0
        while i < len(A_     ):
         try:
          UpperCamelCase			:      Union[str, Any] 			=     word.index(A_ ,							A_     )
         except ValueError:
          new_word.extend(word[i:]     )
          break
         else:
          new_word.extend(word[i:j]     )
          UpperCamelCase			:      Optional[int] 			=     j

         if word[i] == first and i < len(A_     ) - 1 and word[i + 1] == second:
          new_word.append(first + second     )
          i += 2
         else:
          new_word.append(word[i]     )
          i += 1
        UpperCamelCase			:      List[Any] 			=     tuple(A_     )
        UpperCamelCase			:      Optional[Any] 			=     new_word
        if len(A_     ) == 1:
         break
        else:
         UpperCamelCase			:      Dict 			=     get_pairs(A_     )
       UpperCamelCase			:      List[str] 			=     ''' '''.join(A_     )
       UpperCamelCase			:      int 			=     word
       return word





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     []
       for token in re.findall(self.pat ,							A_     ):
        UpperCamelCase			:      List[str] 			=     ''''''.join(
            self.byte_encoder[b] for b in token.encode("utf-8"     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_     ).split(" "     )     )
       return bpe_tokens





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.encoder.get(A_ ,							self.encoder.get(self.unk_token     )     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.decoder.get(A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     ''''''.join(A_     )
       UpperCamelCase			:      List[Any] 			=     bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8" ,							errors=self.errors     )
       return text





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       if not os.path.isdir(A_     ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
       UpperCamelCase			:      Optional[Any] 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Optional[Any] 			=     os.path.join(
           A_ ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

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

       UpperCamelCase			:      Optional[int] 			=     0
       with open(A_ ,							"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 A_     : 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!"     )
          UpperCamelCase			:      int 			=     token_index
         writer.write(" ".join(A_     ) + "\n"     )
         index += 1

       return vocab_file, merge_file





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = False     ):
       '''simple docstring'''




       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=A_ ,							token_ids_a=A_ ,							already_has_special_tokens=A_     )

       if token_ids_a is None:
        return [1] + ([0] * len(A_     )) + [1]
       return [1] + ([0] * len(A_     )) + [1, 1] + ([0] * len(A_     )) + [1]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      Optional[Any] 			=     [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]





      def  __UpperCamelCase(				self ,							A_ ,							A_=False ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     kwargs.pop("add_prefix_space" ,							self.add_prefix_space     )
       if (is_split_into_words or add_prefix_space) and (len(A_     ) > 0 and not text[0].isspace()):
        UpperCamelCase			:      Dict 			=     ''' ''' + text
       return (text, kwargs)





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       return token_ids_a + [self.eos_token_id]





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     []
       for is_user, text in conversation.iter_texts():
        if is_user:
         # We need to space prefix as it's being done within blenderbot
         inputs.append(" " + text     )
        else:
         # Generated responses should contain them already.
         inputs.append(A_     )

       UpperCamelCase			:      List[Any] 			=     '''  '''.join(A_     )
       UpperCamelCase			:      Tuple 			=     self.encode(A_     )
       if len(A_     ) > self.model_max_length:
        UpperCamelCase			:      Optional[Any] 			=     input_ids[-self.model_max_length :]
        logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""     )
       return input_ids







import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch



def 	A_       (  _lowerCAmelCase  )		->				Union[str, Any]:  # picklable for multiprocessing
 return x.sum()



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:  # picklable for multiprocessing
 return i + 1



@dataclass
class       A__     :
      _UpperCAmelCase			:int
      _UpperCAmelCase			:str



class       A__     ( __snake_case    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     {}
       UpperCamelCase			:      Optional[Any] 			=     []
       UpperCamelCase			:      List[Any] 			=     1
       UpperCamelCase			:      Tuple 			=     [1, 2]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      Optional[Any] 			=     {"a": [1, 2], "b": [3, 4]}
       UpperCamelCase			:      Any 			=     {"a": {"1": 1}, "b": 2}
       UpperCamelCase			:      List[str] 			=     {"a": 1, "b": 2, "c": 3, "d": 4}
       UpperCamelCase			:      Dict 			=     {}
       UpperCamelCase			:      Any 			=     []
       UpperCamelCase			:      Any 			=     2
       UpperCamelCase			:      Any 			=     [2, 3]
       UpperCamelCase			:      Optional[Any] 			=     {"a": 2, "b": 3}
       UpperCamelCase			:      List[Any] 			=     {"a": [2, 3], "b": [4, 5]}
       UpperCamelCase			:      Tuple 			=     {"a": {"1": 2}, "b": 3}
       UpperCamelCase			:      Dict 			=     {"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_     )

       UpperCamelCase			:      List[str] 			=     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_     )

       UpperCamelCase			:      List[str] 			=     {"a": np.eye(2     ), "b": np.zeros(3     ), "c": np.ones(2     )}
       UpperCamelCase			:      int 			=     {"a": 2, "b": 0, "c": 2}
       UpperCamelCase			:      Union[str, Any] 			=     {
           "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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     {"a": 1, "b": 2}
       UpperCamelCase			:      List[Any] 			=     {"a": 3, "b": 4}
       UpperCamelCase			:      Tuple 			=     {"a": 5, "b": 6}
       UpperCamelCase			:      Union[str, Any] 			=     sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]     )
       self.assertEqual(sorted(zip_dict(A_ ,							A_ ,							A_     )     ) ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       class       A__     :
             _UpperCAmelCase			:str							=     'bar'


       UpperCamelCase			:      List[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 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[Any]:
 with patch("datasets.utils.py_utils._single_map_nested"  ) as mock_single_map_nested, patch(
     "datasets.parallel.parallel.Pool"  ) as mock_multiprocessing_pool:
  UpperCamelCase			:      Union[str, Any] 			=     {F"""{i}""": i for i in range(_lowerCAmelCase  )}
  UpperCamelCase			:      List[str] 			=     map_nested(lambda _lowerCAmelCase  : x + 10							,     _lowerCAmelCase							,     num_proc=_lowerCAmelCase							,     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__     ( __snake_case    ):





      @require_tf
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       import tensorflow as tf
       from tensorflow.keras import layers

       UpperCamelCase			:      int 			=     layers.Dense(2     )

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

       with temp_seed(42 ,							set_tensorflow=A_     ):
        UpperCamelCase			:      List[Any] 			=     gen_random_output()
       with temp_seed(42 ,							set_tensorflow=A_     ):
        UpperCamelCase			:      Dict 			=     gen_random_output()
       UpperCamelCase			:      Optional[int] 			=     gen_random_output()

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





      @require_torch
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       import torch

       def gen_random_output():
        UpperCamelCase			:      Optional[Any] 			=     torch.nn.Linear(3 ,							2     )
        UpperCamelCase			:      Dict 			=     torch.rand(1 ,							3     )
        return model(A_     ).detach().numpy()

       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Dict 			=     gen_random_output()
       with temp_seed(42 ,							set_pytorch=A_     ):
        UpperCamelCase			:      Optional[int] 			=     gen_random_output()
       UpperCamelCase			:      List[Any] 			=     gen_random_output()

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

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

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



@pytest.mark.parametrize("input_data"							,     [{}]  )
def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Optional[Any] 			=     NestedDataStructure(_lowerCAmelCase  ).data
 assert output_data == input_data



@pytest.mark.parametrize(
    "data, expected_output"							,     [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]							,     )
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Tuple:
 UpperCamelCase			:      Dict 			=     NestedDataStructure(_lowerCAmelCase  ).flatten()
 assert output == expected_output



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      str 			=     A(x=1							,     y="foobar"  )
 UpperCamelCase			:      Tuple 			=     {"x": 1, "y": "foobar"}
 assert asdict(_lowerCAmelCase  ) == expected_output

 UpperCamelCase			:      List[str] 			=     {"a": {"b": A(x=10							,     y="foo"  )}, "c": [A(x=20							,     y="bar"  )]}
 UpperCamelCase			:      Tuple 			=     {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
 assert asdict(_lowerCAmelCase  ) == expected_output

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



def 	A_       (  _lowerCAmelCase  )		->				Tuple:
 return text.split()



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 yield (time.time(), content)
 time.sleep(2  )
 yield (time.time(), content)



def 	A_       (  )		->				str:
 with Pool(2  ) as pool:
  UpperCamelCase			:      List[str] 			=     list(iflatmap_unordered(_lowerCAmelCase							,     _split_text							,     kwargs_iterable=[{"text": "hello there"}] * 10  )  )
  assert out.count("hello"  ) == 10
  assert out.count("there"  ) == 10
  assert len(_lowerCAmelCase  ) == 20

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

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