






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class     a_						(      a__    ):
  lowercase							=				(DEISMultistepScheduler,)
  lowercase							=				(("""num_inference_steps""", 25),)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {
            "num_train_timesteps": 1000,
            "beta_start": 0.0_0_0_1,
            "beta_end": 0.0_2,
            "beta_schedule": "linear",
            "solver_order": 2,
        }

        config.update(**lowerCAmelCase__				)
        return config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=0      ,		**_SCREAMING_SNAKE_CASE				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = dict(self.forward_default_kwargs				)
        UpperCamelCase      = kwargs.pop("""num_inference_steps"""      ,		lowerCAmelCase__				)
        UpperCamelCase      = self.dummy_sample
        UpperCamelCase      = 0.1 * sample
        UpperCamelCase      = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
              UpperCamelCase      = self.get_scheduler_config(**lowerCAmelCase__				)
              UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)
              scheduler.set_timesteps(lowerCAmelCase__				)
              # copy over dummy past residuals
              UpperCamelCase      = dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__				)
                    UpperCamelCase      = scheduler_class.from_pretrained(lowerCAmelCase__				)
                    new_scheduler.set_timesteps(lowerCAmelCase__				)
                    # copy over dummy past residuals
                    UpperCamelCase      = dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCamelCase      = sample, sample
              for t in range(lowerCAmelCase__      ,		time_step + scheduler.config.solver_order + 1				):
                    UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample
                    UpperCamelCase      = new_scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample

                    assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        pass

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=0      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = dict(self.forward_default_kwargs				)
        UpperCamelCase      = kwargs.pop("""num_inference_steps"""      ,		lowerCAmelCase__				)
        UpperCamelCase      = self.dummy_sample
        UpperCamelCase      = 0.1 * sample
        UpperCamelCase      = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
              UpperCamelCase      = self.get_scheduler_config()
              UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)
              scheduler.set_timesteps(lowerCAmelCase__				)

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCamelCase      = dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__				)
                    UpperCamelCase      = scheduler_class.from_pretrained(lowerCAmelCase__				)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(lowerCAmelCase__				)

                    # copy over dummy past residual (must be after setting timesteps)
                    UpperCamelCase      = dummy_past_residuals[: new_scheduler.config.solver_order]

              UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample
              UpperCamelCase      = new_scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample

              assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        if scheduler is None:
              UpperCamelCase      = self.scheduler_classes[0]
              UpperCamelCase      = self.get_scheduler_config(**lowerCAmelCase__				)
              UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)

        UpperCamelCase      = self.scheduler_classes[0]
        UpperCamelCase      = self.get_scheduler_config(**lowerCAmelCase__				)
        UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)

        UpperCamelCase      = 10
        UpperCamelCase      = self.dummy_model()
        UpperCamelCase      = self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__				)

        for i, t in enumerate(scheduler.timesteps				):
              UpperCamelCase      = model(lowerCAmelCase__      ,		lowerCAmelCase__				)
              UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__				).prev_sample

        return sample

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = dict(self.forward_default_kwargs				)

        UpperCamelCase      = kwargs.pop("""num_inference_steps"""      ,		lowerCAmelCase__				)

        for scheduler_class in self.scheduler_classes:
              UpperCamelCase      = self.get_scheduler_config()
              UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)

              UpperCamelCase      = self.dummy_sample
              UpperCamelCase      = 0.1 * sample

              if num_inference_steps is not None and hasattr(lowerCAmelCase__      ,		"""set_timesteps"""				):
                    scheduler.set_timesteps(lowerCAmelCase__				)
              elif num_inference_steps is not None and not hasattr(lowerCAmelCase__      ,		"""set_timesteps"""				):
                    UpperCamelCase      = num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCamelCase      = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
              UpperCamelCase      = dummy_past_residuals[: scheduler.config.solver_order]

              UpperCamelCase      = scheduler.timesteps[5]
              UpperCamelCase      = scheduler.timesteps[6]

              UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample
              UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		**lowerCAmelCase__				).prev_sample

              self.assertEqual(output_a.shape      ,		sample.shape				)
              self.assertEqual(output_a.shape      ,		output_a.shape				)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = DEISMultistepScheduler(**self.get_scheduler_config()				)
        UpperCamelCase      = self.full_loop(scheduler=lowerCAmelCase__				)
        UpperCamelCase      = torch.mean(torch.abs(lowerCAmelCase__				)				)

        assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

        UpperCamelCase      = DPMSolverSinglestepScheduler.from_config(scheduler.config				)
        UpperCamelCase      = DPMSolverMultistepScheduler.from_config(scheduler.config				)
        UpperCamelCase      = UniPCMultistepScheduler.from_config(scheduler.config				)
        UpperCamelCase      = DEISMultistepScheduler.from_config(scheduler.config				)

        UpperCamelCase      = self.full_loop(scheduler=lowerCAmelCase__				)
        UpperCamelCase      = torch.mean(torch.abs(lowerCAmelCase__				)				)

        assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase__				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        self.check_over_configs(thresholding=lowerCAmelCase__				)
        for order in [1, 2, 3]:
              for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                          for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=lowerCAmelCase__      ,		prediction_type=lowerCAmelCase__      ,		sample_max_value=lowerCAmelCase__      ,		algorithm_type="""deis"""      ,		solver_order=lowerCAmelCase__      ,		solver_type=lowerCAmelCase__      ,		)

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCAmelCase__				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        for algorithm_type in ["deis"]:
              for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                          for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=lowerCAmelCase__      ,		solver_type=lowerCAmelCase__      ,		prediction_type=lowerCAmelCase__      ,		algorithm_type=lowerCAmelCase__      ,		)
                                UpperCamelCase      = self.full_loop(
                                    solver_order=lowerCAmelCase__      ,		solver_type=lowerCAmelCase__      ,		prediction_type=lowerCAmelCase__      ,		algorithm_type=lowerCAmelCase__      ,		)
                                assert not torch.isnan(lowerCAmelCase__				).any(), "Samples have nan numbers"

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        self.check_over_configs(lower_order_final=lowerCAmelCase__				)
        self.check_over_configs(lower_order_final=lowerCAmelCase__				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=lowerCAmelCase__      ,		time_step=0				)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.full_loop()
        UpperCamelCase      = torch.mean(torch.abs(lowerCAmelCase__				)				)

        assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.full_loop(prediction_type="""v_prediction"""				)
        UpperCamelCase      = torch.mean(torch.abs(lowerCAmelCase__				)				)

        assert abs(result_mean.item() - 0.0_9_1				) < 1e-3


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.scheduler_classes[0]
        UpperCamelCase      = self.get_scheduler_config(thresholding=lowerCAmelCase__      ,		dynamic_thresholding_ratio=0				)
        UpperCamelCase      = scheduler_class(**lowerCAmelCase__				)

        UpperCamelCase      = 10
        UpperCamelCase      = self.dummy_model()
        UpperCamelCase      = self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCAmelCase__				)

        for i, t in enumerate(scheduler.timesteps				):
              UpperCamelCase      = model(lowerCAmelCase__      ,		lowerCAmelCase__				)
              UpperCamelCase      = scheduler.step(lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__				).prev_sample

        assert sample.dtype == torch.floataa








'''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    lowercase__			(				__UpperCamelCase   )->			Dict:  # picklable for multiprocessing
      return x.sum()







def    lowercase__			(				__UpperCamelCase   )->			Tuple:  # picklable for multiprocessing
      return i + 1






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42






class     a_						(      lowerCamelCase    ):

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 1
        UpperCamelCase      = [1, 2]
        UpperCamelCase      = {"""a""": 1, """b""": 2}
        UpperCamelCase      = {"""a""": [1, 2], """b""": [3, 4]}
        UpperCamelCase      = {"""a""": {"""1""": 1}, """b""": 2}
        UpperCamelCase      = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
        UpperCamelCase      = {}
        UpperCamelCase      = []
        UpperCamelCase      = 2
        UpperCamelCase      = [2, 3]
        UpperCamelCase      = {"""a""": 2, """b""": 3}
        UpperCamelCase      = {"""a""": [2, 3], """b""": [4, 5]}
        UpperCamelCase      = {"""a""": {"""1""": 2}, """b""": 3}
        UpperCamelCase      = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = 2
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		num_proc=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)

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

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






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


  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        class     a_						:
          lowercase							=				"""bar"""


        UpperCamelCase      = Foo()
        self.assertEqual(foo.my_attr      ,		"""bar"""				)
        with temporary_assignment(_SCREAMING_SNAKE_CASE      ,		"""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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      with patch("""datasets.utils.py_utils._single_map_nested"""   ) as mock_single_map_nested, patch(
          """datasets.parallel.parallel.Pool"""   ) as mock_multiprocessing_pool:
            UpperCamelCase      = {F"{i}": i for i in range(__UpperCamelCase   )}
            UpperCamelCase      = 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     a_						(      lowerCamelCase    ):

  @require_tf
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        import tensorflow as tf
        from tensorflow.keras import layers

        UpperCamelCase      = layers.Dense(2				)

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

        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_tensorflow=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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

  @require_torch
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        import torch

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

        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        with temp_seed(42      ,		set_pytorch=_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = gen_random_output()
        UpperCamelCase      = gen_random_output()

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


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






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

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

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







@pytest.mark.parametrize("""input_data"""		,						[{}]   )
def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = NestedDataStructure(__UpperCamelCase   ).flatten()
      assert output == expected_output







def    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = A(x=1		,						y="""foobar"""   )
      UpperCamelCase      = {"""x""": 1, """y""": """foobar"""}
      assert asdict(__UpperCamelCase   ) == expected_output

      UpperCamelCase      = {"""a""": {"""b""": A(x=10		,						y="""foo"""   )}, """c""": [A(x=20		,						y="""bar"""   )]}
      UpperCamelCase      = {"""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    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      return text.split()







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      yield (time.time(), content)
      time.sleep(2   )
      yield (time.time(), content)







def    lowercase__			(				)->			int:
      with Pool(2   ) as pool:
            UpperCamelCase      = 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:
            UpperCamelCase      = 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:
            UpperCamelCase      = []
            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
