


"""simple docstring"""

import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
       from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileNetVaImageProcessor





class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						self.config_class(**self.inputs_dict				)
       self.parent.assertTrue(hasattr(A_   ,							"""tf_padding"""				)				)
       self.parent.assertTrue(hasattr(A_   ,							"""depth_multiplier"""				)				)






class  lowerCamelCase__    :
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						Tuple=1_3   ,							A_						:						Optional[int]=3   ,							A_						:						str=3_2   ,							A_						:						Dict=0.25   ,							A_						:						Any=8   ,							A_						:						Tuple=8   ,							A_						:						Union[str, Any]=6   ,							A_						:						List[Any]=3_2   ,							A_						:						List[Any]=True   ,							A_						:						str=True   ,							A_						:						Optional[int]=True   ,							A_						:						Dict="relu6"   ,							A_						:						List[str]=1_2_8_0   ,							A_						:						Dict=0.1   ,							A_						:						Optional[Any]=0.02   ,							A_						:						str=True   ,							A_						:						Optional[int]=True   ,							A_						:						int=1_0   ,							A_						:						List[Any]=None   ,							):






       '''simple docstring'''


       __lowercase						=						parent
       __lowercase						=						batch_size
       __lowercase						=						num_channels
       __lowercase						=						image_size
       __lowercase						=						depth_multiplier
       __lowercase						=						depth_divisible_by
       __lowercase						=						min_depth
       __lowercase						=						expand_ratio
       __lowercase						=						tf_padding
       __lowercase						=						output_stride
       __lowercase						=						first_layer_is_expansion
       __lowercase						=						finegrained_output
       __lowercase						=						hidden_act
       __lowercase						=						last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier				)
       __lowercase						=						classifier_dropout_prob
       __lowercase						=						use_labels
       __lowercase						=						is_training
       __lowercase						=						num_labels
       __lowercase						=						initializer_range
       __lowercase						=						scope
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       __lowercase						=						None
       __lowercase						=						None
       if self.use_labels:
        __lowercase						=						ids_tensor([self.batch_size]   ,							self.num_labels				)
        __lowercase						=						ids_tensor([self.batch_size, self.image_size, self.image_size]   ,							self.num_labels				)

       __lowercase						=						self.get_config()

       return config, pixel_values, labels, pixel_labels
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       return MobileNetVaConfig(
           num_channels=self.num_channels   ,							image_size=self.image_size   ,							depth_multiplier=self.depth_multiplier   ,							depth_divisible_by=self.depth_divisible_by   ,							min_depth=self.min_depth   ,							expand_ratio=self.expand_ratio   ,							output_stride=self.output_stride   ,							first_layer_is_expansion=self.first_layer_is_expansion   ,							finegrained_output=self.finegrained_output   ,							hidden_act=self.hidden_act   ,							tf_padding=self.tf_padding   ,							classifier_dropout_prob=self.classifier_dropout_prob   ,							initializer_range=self.initializer_range   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						int   ,							A_						:						Optional[Any]   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MobileNetVaModel(config=A_				)
       model.to(A_				)
       model.eval()
       __lowercase						=						model(A_				)
       self.parent.assertEqual(
           result.last_hidden_state.shape   ,							(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )   ,							)
       self.parent.assertEqual(
           result.pooler_output.shape   ,							(self.batch_size, self.last_hidden_size)   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]   ,							A_						:						int   ,							A_						:						Any   ,							A_						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						self.num_labels
       __lowercase						=						MobileNetVaForImageClassification(A_				)
       model.to(A_				)
       model.eval()
       __lowercase						=						model(A_   ,							labels=A_				)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Optional[Any]   ,							A_						:						Tuple   ,							A_						:						Union[str, Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.num_labels
       __lowercase						=						MobileNetVaForSemanticSegmentation(A_				)
       model.to(A_				)
       model.eval()
       __lowercase						=						model(A_				)
       self.parent.assertEqual(
           result.logits.shape   ,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )   ,							)
       __lowercase						=						model(A_   ,							labels=A_				)
       self.parent.assertEqual(
           result.logits.shape   ,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						self.prepare_config_and_inputs()
       __lowercase				,		__lowercase				,		__lowercase				,		__lowercase						=						config_and_inputs
       __lowercase						=						{"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  str           =		(
          (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      a			:  Tuple           =		(
          {
              """feature-extraction""": MobileNetVaModel,
              """image-classification""": MobileNetVaForImageClassification,
              """image-segmentation""": MobileNetVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      a			:  int           =		False
      a			:  int           =		False
      a			:  Dict           =		False
      a			:  Any           =		False
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						MobileNetVaModelTester(self				)
       __lowercase						=						MobileNetVaConfigTester(self   ,							config_class=A_   ,							has_text_modality=A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      @unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       pass
      @unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       pass
      @unittest.skip(reason="""MobileNetV2 does not output attentions"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase						=						model_class(A_				)
        __lowercase						=						inspect.signature(model.forward				)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowercase						=						[*signature.parameters.keys()]

        __lowercase						=						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       def check_hidden_states_output(A_						:						Optional[int]   ,							A_						:						Optional[Any]   ,							A_						:						Union[str, Any]				):
        __lowercase						=						model_class(A_				)
        model.to(A_				)
        model.eval()

        with torch.no_grad():
         __lowercase						=						model(**self._prepare_for_class(A_   ,							A_				)				)

        __lowercase						=						outputs.hidden_states

        __lowercase						=						1_6
        self.assertEqual(len(A_				)   ,							A_				)

       __lowercase				,		__lowercase						=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase						=						True
        check_hidden_states_output(A_   ,							A_   ,							A_				)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowercase						=						True

        check_hidden_states_output(A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase						=						MobileNetVaModel.from_pretrained(A_				)
        self.assertIsNotNone(A_				)






def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
 return image





@require_torch
@require_vision
class  lowerCamelCase__    (		unittest.TestCase     ):
      @cached_property
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       return (
           MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""				) if is_vision_available() else None
       )
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""				).to(A_				)

       __lowercase						=						self.default_image_processor
       __lowercase						=						prepare_img()
       __lowercase						=						image_processor(images=A_   ,							return_tensors="""pt"""				).to(A_				)

       # forward pass
       with torch.no_grad():
        __lowercase						=						model(**A_				)

       # verify the logits
       __lowercase						=						torch.Size((1, 1_0_0_1)				)
       self.assertEqual(outputs.logits.shape   ,							A_				)

       __lowercase						=						torch.tensor([0.24_45, -1.19_93, 0.19_05]				).to(A_				)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,							A_   ,							atol=1e-4				)				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""				)
       __lowercase						=						model.to(A_				)

       __lowercase						=						MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""				)

       __lowercase						=						prepare_img()
       __lowercase						=						image_processor(images=A_   ,							return_tensors="""pt"""				).to(A_				)

       # forward pass
       with torch.no_grad():
        __lowercase						=						model(**A_				)
       __lowercase						=						outputs.logits

       # verify the logits
       __lowercase						=						torch.Size((1, 2_1, 6_5, 6_5)				)
       self.assertEqual(logits.shape   ,							A_				)

       __lowercase						=						torch.tensor(
           [
               [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
               [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
               [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
           ]   ,							device=A_   ,							)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,							A_   ,							atol=1e-4				)				)




"""simple docstring"""

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

import multiprocess
import numpy as np
import pytest

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

from .utils import require_tf, require_torch


def lowerCAmelCase_   (					UpperCamelCase__						:			int			):  # picklable for multiprocessing






 """simple docstring"""







 return x.sum()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]			):  # picklable for multiprocessing






 """simple docstring"""







 return i + 1





@dataclass
class  lowerCamelCase__    :
      a			:  int
      a			:  str






class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''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(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_				)

       __lowercase						=						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_				)

       __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(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 SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''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(A_   ,							A_   ,							A_				)				)   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       class  lowerCamelCase__    :
             a			:  int           =		"""bar"""






       __lowercase						=						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 lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 with patch("""datasets.utils.py_utils._single_map_nested"""			) as mock_single_map_nested, patch(
     """datasets.parallel.parallel.Pool"""			) as mock_multiprocessing_pool:
  __lowercase						=						{f'''{i}''': i for i in range(UpperCamelCase__			)}
  __lowercase						=						map_nested(lambda UpperCamelCase__			: x + 10					, UpperCamelCase__					, num_proc=UpperCamelCase__					, 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__    (		_a     ):
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''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(A_				).numpy()

       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       import torch

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

       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


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

       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






@pytest.mark.parametrize("""input_data"""					, [{}]			)
def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).data
 assert output_data == input_data


@pytest.mark.parametrize(
    """data, expected_output"""					, [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]					, )
def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).flatten()
 assert output == expected_output


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						A(x=1					, y="""foobar"""			)
 __lowercase						=						{"""x""": 1, """y""": """foobar"""}
 assert asdict(UpperCamelCase__			) == 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(UpperCamelCase__			) == expected_output

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


def lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 return text.split()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict			):






 """simple docstring"""







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


def lowerCAmelCase_   (					):






 """simple docstring"""







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

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

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