





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:int							=     CodeGenTokenizer
      _UpperCAmelCase			:Dict							=     CodeGenTokenizerFast
      _UpperCAmelCase			:Any							=     True
      _UpperCAmelCase			:Any							=     {'add_prefix_space': True}
      _UpperCAmelCase			:Optional[int]							=     False





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




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase			:      Dict 			=     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
           "<|endoftext|>",
       ]
       UpperCamelCase			:      List[str] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Union[str, Any] 			=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase			:      Optional[int] 			=     {"unk_token": "<unk>"}

       UpperCamelCase			:      str 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      str 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["merges_file"]     )
       with open(self.vocab_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write(json.dumps(A_     ) + "\n"     )
       with open(self.merges_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write("\n".join(A_     )     )





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




       kwargs.update(self.special_tokens_map     )
       return CodeGenTokenizer.from_pretrained(self.tmpdirname ,							**A_     )





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




       kwargs.update(self.special_tokens_map     )
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,							**A_     )





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




       UpperCamelCase			:      Tuple 			=     "lower newer"
       UpperCamelCase			:      Dict 			=     "lower newer"
       return input_text, output_text





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




       UpperCamelCase			:      str 			=     CodeGenTokenizer(self.vocab_file ,							self.merges_file ,							**self.special_tokens_map     )
       UpperCamelCase			:      Tuple 			=     "lower newer"
       UpperCamelCase			:      Optional[Any] 			=     ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
       UpperCamelCase			:      List[Any] 			=     tokenizer.tokenize(A_ ,							add_prefix_space=A_     )
       self.assertListEqual(A_ ,							A_     )

       UpperCamelCase			:      Dict 			=     tokens + [tokenizer.unk_token]
       UpperCamelCase			:      Optional[Any] 			=     [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     ) ,							A_     )





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




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase			:      Any 			=     self.get_tokenizer()
       UpperCamelCase			:      int 			=     self.get_rust_tokenizer(add_prefix_space=A_     )

       UpperCamelCase			:      Dict 			=     "lower newer"

       # Testing tokenization
       UpperCamelCase			:      Tuple 			=     tokenizer.tokenize(A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      int 			=     rust_tokenizer.tokenize(A_     )
       self.assertListEqual(A_ ,							A_     )

       # Testing conversion to ids without special tokens
       UpperCamelCase			:      Optional[int] 			=     tokenizer.encode(A_ ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      Tuple 			=     rust_tokenizer.encode(A_ ,							add_special_tokens=A_     )
       self.assertListEqual(A_ ,							A_     )

       # Testing conversion to ids with special tokens
       UpperCamelCase			:      List[str] 			=     self.get_rust_tokenizer(add_prefix_space=A_     )
       UpperCamelCase			:      Tuple 			=     tokenizer.encode(A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      List[Any] 			=     rust_tokenizer.encode(A_     )
       self.assertListEqual(A_ ,							A_     )

       # Testing the unknown token
       UpperCamelCase			:      Any 			=     tokens + [rust_tokenizer.unk_token]
       UpperCamelCase			:      str 			=     [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_     ) ,							A_     )





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




       pass





      def  __UpperCamelCase(				self ,							A_=15     ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
         UpperCamelCase			:      Any 			=     self.rust_tokenizer_class.from_pretrained(A_ ,							**A_     )

         # Simple input
         UpperCamelCase			:      Any 			=     "This is a simple input"
         UpperCamelCase			:      Optional[int] 			=     ["This is a simple input 1", "This is a simple input 2"]
         UpperCamelCase			:      Optional[int] 			=     ("This is a simple input", "This is a pair")
         UpperCamelCase			:      Dict 			=     [
             ("This is a simple input 1", "This is a simple input 2"),
             ("This is a simple pair 1", "This is a simple pair 2"),
         ]

         # Simple input tests
         self.assertRaises(A_ ,							tokenizer_r.encode ,							A_ ,							max_length=A_ ,							padding="max_length"     )

         # Simple input
         self.assertRaises(A_ ,							tokenizer_r.encode_plus ,							A_ ,							max_length=A_ ,							padding="max_length"     )

         # Simple input
         self.assertRaises(
             A_ ,							tokenizer_r.batch_encode_plus ,							A_ ,							max_length=A_ ,							padding="max_length" ,							)

         # Pair input
         self.assertRaises(A_ ,							tokenizer_r.encode ,							A_ ,							max_length=A_ ,							padding="max_length"     )

         # Pair input
         self.assertRaises(A_ ,							tokenizer_r.encode_plus ,							A_ ,							max_length=A_ ,							padding="max_length"     )

         # Pair input
         self.assertRaises(
             A_ ,							tokenizer_r.batch_encode_plus ,							A_ ,							max_length=A_ ,							padding="max_length" ,							)





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




       UpperCamelCase			:      Optional[int] 			=     CodeGenTokenizer.from_pretrained(self.tmpdirname ,							pad_token="<pad>"     )

       # Simple input
       UpperCamelCase			:      Optional[int] 			=     "This is a simple input"
       UpperCamelCase			:      Dict 			=     ["This is a simple input looooooooong", "This is a simple input"]
       UpperCamelCase			:      Optional[int] 			=     ("This is a simple input", "This is a pair")
       UpperCamelCase			:      Optional[Any] 			=     [
           ("This is a simple input loooooong", "This is a simple input"),
           ("This is a simple pair loooooong", "This is a simple pair"),
       ]

       UpperCamelCase			:      int 			=     tokenizer.pad_token_id

       UpperCamelCase			:      List[str] 			=     tokenizer(A_ ,							padding="max_length" ,							max_length=30 ,							return_tensors="np"     )
       UpperCamelCase			:      List[Any] 			=     tokenizer(A_ ,							padding=A_ ,							truncate=A_ ,							return_tensors="np"     )
       UpperCamelCase			:      List[str] 			=     tokenizer(*A_ ,							padding="max_length" ,							max_length=60 ,							return_tensors="np"     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer(A_ ,							padding=A_ ,							truncate=A_ ,							return_tensors="np"     )

       # s
       # test single string max_length padding
       self.assertEqual(out_s["input_ids"].shape[-1] ,							30     )
       self.assertTrue(pad_token_id in out_s["input_ids"]     )
       self.assertTrue(0 in out_s["attention_mask"]     )

       # s2
       # test automatic padding
       self.assertEqual(out_sa["input_ids"].shape[-1] ,							33     )
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["input_ids"][0]     )
       self.assertFalse(0 in out_sa["attention_mask"][0]     )
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["input_ids"][1]     )
       self.assertTrue(0 in out_sa["attention_mask"][1]     )

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["input_ids"].shape[-1] ,							60     )
       self.assertTrue(pad_token_id in out_p["input_ids"]     )
       self.assertTrue(0 in out_p["attention_mask"]     )

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["input_ids"].shape[-1] ,							52     )
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["input_ids"][0]     )
       self.assertFalse(0 in out_pa["attention_mask"][0]     )
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["input_ids"][1]     )
       self.assertTrue(0 in out_pa["attention_mask"][1]     )





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




       UpperCamelCase			:      Tuple 			=     "$$$"
       UpperCamelCase			:      str 			=     CodeGenTokenizer.from_pretrained(self.tmpdirname ,							bos_token=A_ ,							add_bos_token=A_     )

       UpperCamelCase			:      Tuple 			=     "This is a simple input"
       UpperCamelCase			:      str 			=     ["This is a simple input 1", "This is a simple input 2"]

       UpperCamelCase			:      Tuple 			=     tokenizer.bos_token_id

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer(A_     )
       UpperCamelCase			:      int 			=     tokenizer(A_     )

       self.assertEqual(out_s.input_ids[0] ,							A_     )
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

       UpperCamelCase			:      int 			=     tokenizer.decode(out_s.input_ids     )
       UpperCamelCase			:      Optional[Any] 			=     tokenizer.batch_decode(out_sa.input_ids     )

       self.assertEqual(decode_s.split()[0] ,							A_     )
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )





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




       UpperCamelCase			:      int 			=     CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"     )

       UpperCamelCase			:      List[str] 			=     "\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
       UpperCamelCase			:      Optional[int] 			=     "\nif len_a > len_b:      result = a\nelse:      result = b"

       UpperCamelCase			:      int 			=     tokenizer.encode(A_     )
       UpperCamelCase			:      int 			=     ["^#", re.escape("<|endoftext|>"     ), "^'''", "^\"\"\"", "\n\n\n"]
       UpperCamelCase			:      Any 			=     tokenizer.decode(A_ ,							truncate_before_pattern=A_     )
       self.assertEqual(A_ ,							A_     )





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




       pass







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
