





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetaImageProcessor





class  lowerCAmelCase							(       unittest.TestCase ):







  def __init__(      self  :str   ,							_lowercase  :Optional[Any]   ,							_lowercase  :str=7   ,							_lowercase  :Tuple=3   ,							_lowercase  :Dict=30   ,							_lowercase  :Any=4_00   ,							_lowercase  :List[Any]=True   ,							_lowercase  :List[str]=None   ,							_lowercase  :str=True   ,							_lowercase  :str=[0.5, 0.5, 0.5]   ,							_lowercase  :Tuple=[0.5, 0.5, 0.5]   ,							_lowercase  :List[str]=True   ,							_lowercase  :List[Any]=1 / 2_55   ,							_lowercase  :Optional[Any]=True   ,							):






     '''simple docstring'''





     lowercase__			       =  size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  num_channels
     lowercase__			       =  min_resolution
     lowercase__			       =  max_resolution
     lowercase__			       =  do_resize
     lowercase__			       =  size
     lowercase__			       =  do_normalize
     lowercase__			       =  image_mean
     lowercase__			       =  image_std
     lowercase__			       =  do_rescale
     lowercase__			       =  rescale_factor
     lowercase__			       =  do_pad







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Tuple=False ):






     '''simple docstring'''





     if not batched:
        lowercase__			       =  image_inputs[0]
        if isinstance(lowerCAmelCase__   ,							Image.Image ):
           lowercase__			       =  image.size
        else:
           lowercase__			       =  image.shape[1], image.shape[2]
        if w < h:
           lowercase__			       =  int(self.size["shortest_edge"] * h / w )
           lowercase__			       =  self.size["shortest_edge"]
        elif w > h:
           lowercase__			       =  self.size["shortest_edge"]
           lowercase__			       =  int(self.size["shortest_edge"] * w / h )
        else:
           lowercase__			       =  self.size["shortest_edge"]
           lowercase__			       =  self.size["shortest_edge"]

     else:
        lowercase__			       =  []
        for image in image_inputs:
           lowercase__			       =  self.get_expected_values([image] )
           expected_values.append((expected_height, expected_width) )
        lowercase__			       =  max(lowerCAmelCase__   ,							key=lambda _lowercase : item[0] )[0]
        lowercase__			       =  max(lowerCAmelCase__   ,							key=lambda _lowercase : item[1] )[1]

     return expected_height, expected_width





@require_torch
@require_vision
class  lowerCAmelCase							(       a__							,							unittest.TestCase ):
  __lowerCamelCase		=      DetaImageProcessor if is_vision_available() else None







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  DetaImageProcessingTester(self )







  @property
  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"image_mean" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"image_std" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"do_normalize" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"do_resize" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"do_rescale" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"do_pad" ) )
     self.assertTrue(hasattr(lowerCAmelCase__   ,							"size" ) )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size   ,							{"shortest_edge": 18, "longest_edge": 13_33} )
     self.assertEqual(image_processor.do_pad   ,							lowerCAmelCase__ )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowerCAmelCase__ )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase__   ,							Image.Image )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" ).pixel_values

     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__ )

     self.assertEqual(
         encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

     # Test batched
     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,							batched=lowerCAmelCase__ )

     lowercase__			       =  image_processing(lowerCAmelCase__   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,							)







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowerCAmelCase__   ,							numpify=lowerCAmelCase__ )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase__   ,							np.ndarray )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" ).pixel_values

     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__ )

     self.assertEqual(
         encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

     # Test batched
     lowercase__			       =  image_processing(lowerCAmelCase__   ,							return_tensors="pt" ).pixel_values

     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,							batched=lowerCAmelCase__ )

     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,							)







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowerCAmelCase__   ,							torchify=lowerCAmelCase__ )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase__   ,							torch.Tensor )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" ).pixel_values

     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__ )

     self.assertEqual(
         encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

     # Test batched
     lowercase__			       =  image_processing(lowerCAmelCase__   ,							return_tensors="pt" ).pixel_values

     lowercase__			       =  self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,							batched=lowerCAmelCase__ )

     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )   ,							)







  @slow
  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,							"r" ) as f:
        lowercase__			       =  json.loads(f.read() )

     lowercase__			       =  {"image_id": 3_97_69, "annotations": target}

     # encode them
     lowercase__			       =  DetaImageProcessor()
     lowercase__			       =  image_processing(images=lowerCAmelCase__   ,							annotations=lowerCAmelCase__   ,							return_tensors="pt" )

     # verify pixel values
     lowercase__			       =  torch.Size([1, 3, 8_00, 10_66] )
     self.assertEqual(encoding["pixel_values"].shape   ,							lowerCAmelCase__ )

     lowercase__			       =  torch.tensor([0.2796, 0.3138, 0.3481] )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,							lowerCAmelCase__   ,							atol=1e-4 ) )

     # verify area
     lowercase__			       =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,							lowerCAmelCase__ ) )
     # verify boxes
     lowercase__			       =  torch.Size([6, 4] )
     self.assertEqual(encoding["labels"][0]["boxes"].shape   ,							lowerCAmelCase__ )
     lowercase__			       =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,							lowerCAmelCase__   ,							atol=1e-3 ) )
     # verify image_id
     lowercase__			       =  torch.tensor([3_97_69] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,							lowerCAmelCase__ ) )
     # verify is_crowd
     lowercase__			       =  torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,							lowerCAmelCase__ ) )
     # verify class_labels
     lowercase__			       =  torch.tensor([75, 75, 63, 65, 17, 17] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,							lowerCAmelCase__ ) )
     # verify orig_size
     lowercase__			       =  torch.tensor([4_80, 6_40] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,							lowerCAmelCase__ ) )
     # verify size
     lowercase__			       =  torch.tensor([8_00, 10_66] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,							lowerCAmelCase__ ) )







  @slow
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,							"r" ) as f:
        lowercase__			       =  json.loads(f.read() )

     lowercase__			       =  {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

     lowercase__			       =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

     # encode them
     lowercase__			       =  DetaImageProcessor(format="coco_panoptic" )
     lowercase__			       =  image_processing(images=lowerCAmelCase__   ,							annotations=lowerCAmelCase__   ,							masks_path=lowerCAmelCase__   ,							return_tensors="pt" )

     # verify pixel values
     lowercase__			       =  torch.Size([1, 3, 8_00, 10_66] )
     self.assertEqual(encoding["pixel_values"].shape   ,							lowerCAmelCase__ )

     lowercase__			       =  torch.tensor([0.2796, 0.3138, 0.3481] )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,							lowerCAmelCase__   ,							atol=1e-4 ) )

     # verify area
     lowercase__			       =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,							lowerCAmelCase__ ) )
     # verify boxes
     lowercase__			       =  torch.Size([6, 4] )
     self.assertEqual(encoding["labels"][0]["boxes"].shape   ,							lowerCAmelCase__ )
     lowercase__			       =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,							lowerCAmelCase__   ,							atol=1e-3 ) )
     # verify image_id
     lowercase__			       =  torch.tensor([3_97_69] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,							lowerCAmelCase__ ) )
     # verify is_crowd
     lowercase__			       =  torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,							lowerCAmelCase__ ) )
     # verify class_labels
     lowercase__			       =  torch.tensor([17, 17, 63, 75, 75, 93] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,							lowerCAmelCase__ ) )
     # verify masks
     lowercase__			       =  82_28_73
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,							lowerCAmelCase__ )
     # verify orig_size
     lowercase__			       =  torch.tensor([4_80, 6_40] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,							lowerCAmelCase__ ) )
     # verify size
     lowercase__			       =  torch.tensor([8_00, 10_66] )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,							lowerCAmelCase__ ) )













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


def _A       (   __magic_name__	):  # picklable for multiprocessing
   return i + 1





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42





class  lowerCAmelCase							(       lowercase_ ):







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  {}
     lowercase__			       =  []
     lowercase__			       =  1
     lowercase__			       =  [1, 2]
     lowercase__			       =  {"a": 1, "b": 2}
     lowercase__			       =  {"a": [1, 2], "b": [3, 4]}
     lowercase__			       =  {"a": {"1": 1}, "b": 2}
     lowercase__			       =  {"a": 1, "b": 2, "c": 3, "d": 4}
     lowercase__			       =  {}
     lowercase__			       =  []
     lowercase__			       =  2
     lowercase__			       =  [2, 3]
     lowercase__			       =  {"a": 2, "b": 3}
     lowercase__			       =  {"a": [2, 3], "b": [4, 5]}
     lowercase__			       =  {"a": {"1": 2}, "b": 3}
     lowercase__			       =  {"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 )

     lowercase__			       =  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 )

     lowercase__			       =  {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
     lowercase__			       =  {"a": 2, "b": 0, "c": 2}
     lowercase__			       =  {
         "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] ):






     '''simple docstring'''





     lowercase__			       =  {"a": 1, "b": 2}
     lowercase__			       =  {"a": 3, "b": 4}
     lowercase__			       =  {"a": 5, "b": 6}
     lowercase__			       =  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
     self.assertEqual(sorted(zip_dict(_lowercase   ,							_lowercase   ,							_lowercase ) )   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     class  lowerCAmelCase							:
       __lowerCamelCase		=      'bar'
     lowercase__			       =  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 _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   with patch("datasets.utils.py_utils._single_map_nested"	) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"	) as mock_multiprocessing_pool:
      lowercase__			       =  {f'''{i}''': i for i in range(__magic_name__	)}
      lowercase__			       =  map_nested(lambda __magic_name__	: x + 10  , __magic_name__  , num_proc=__magic_name__  , 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							(       lowercase_ ):







  @require_tf
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     import tensorflow as tf
     from tensorflow.keras import layers

     lowercase__			       =  layers.Dense(2 )

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

     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  @require_torch
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     import torch

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

     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





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

     with temp_seed(42 ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42 ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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


@pytest.mark.parametrize("input_data"  , [{}]	)
def _A       (   __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).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       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).flatten()
   assert output == expected_output


def _A       (   ):
   lowercase__			       =  A(x=1  , y="foobar"	)
   lowercase__			       =  {"x": 1, "y": "foobar"}
   assert asdict(__magic_name__	) == expected_output

   lowercase__			       =  {"a": {"b": A(x=10  , y="foo"	)}, "c": [A(x=20  , y="bar"	)]}
   lowercase__			       =  {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
   assert asdict(__magic_name__	) == expected_output

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


def _A       (   __magic_name__	):
   return text.split()


def _A       (   __magic_name__	):
   yield (time.time(), content)
   time.sleep(2	)
   yield (time.time(), content)







def _A       (   ):
   with Pool(2	) as pool:
      lowercase__			       =  list(iflatmap_unordered(__magic_name__  , _split_text  , kwargs_iterable=[{"text": "hello there"}] * 10	)	)
      assert out.count("hello"	) == 10
      assert out.count("there"	) == 10
      assert len(__magic_name__	) == 20

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

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






