






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort






class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''

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



        """simple docstring"""






        UpperCamelCase      = floats_tensor((1, 3, 128, 128)      ,		rng=random.Random(_a				)				)
        UpperCamelCase      = np.random.RandomState(_a				)
        UpperCamelCase      = {
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 3,
            """strength""": 0.7_5,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs

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



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]				)
        assert np.abs(image_slice - expected_slice				).max() < 1e-1

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        UpperCamelCase      = PNDMScheduler.from_config(pipe.scheduler.config      ,		skip_prk_steps=_a				)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        UpperCamelCase      = LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
        pipe.set_progress_bar_config(disable=_a				)

        # warmup pass to apply optimizations
        UpperCamelCase      = pipe(**self.get_dummy_inputs()				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1

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



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        UpperCamelCase      = EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        UpperCamelCase      = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1


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



        """simple docstring"""






        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,		provider="""CPUExecutionProvider"""				)
        UpperCamelCase      = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = self.get_dummy_inputs()
        UpperCamelCase      = pipe(**_a				).images
        UpperCamelCase      = image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        UpperCamelCase      = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1






@nightly
@require_onnxruntime
@require_torch_gpu
class     a_						(      unittest.TestCase    ):

  @property
  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = ort.SessionOptions()
        UpperCamelCase      = False
        return options

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



        """simple docstring"""






        UpperCamelCase      = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""				)
        UpperCamelCase      = init_image.resize((768, 512)				)
        # using the PNDM scheduler by default
        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """CompVis/stable-diffusion-v1-4"""      ,		revision="""onnx"""      ,		safety_checker=_a      ,		feature_extractor=_a      ,		provider=self.gpu_provider      ,		sess_options=self.gpu_options      ,		)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = """A fantasy landscape, trending on artstation"""

        UpperCamelCase      = np.random.RandomState(0				)
        UpperCamelCase      = pipe(
            prompt=_a      ,		image=_a      ,		strength=0.7_5      ,		guidance_scale=7.5      ,		num_inference_steps=10      ,		generator=_a      ,		output_type="""np"""      ,		)
        UpperCamelCase      = output.images
        UpperCamelCase      = images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        UpperCamelCase      = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]				)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 2e-2


  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""				)
        UpperCamelCase      = init_image.resize((768, 512)				)
        UpperCamelCase      = LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""      ,		subfolder="""scheduler"""      ,		revision="""onnx"""				)
        UpperCamelCase      = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""      ,		revision="""onnx"""      ,		scheduler=_a      ,		safety_checker=_a      ,		feature_extractor=_a      ,		provider=self.gpu_provider      ,		sess_options=self.gpu_options      ,		)
        pipe.set_progress_bar_config(disable=_a				)

        UpperCamelCase      = """A fantasy landscape, trending on artstation"""

        UpperCamelCase      = np.random.RandomState(0				)
        UpperCamelCase      = pipe(
            prompt=_a      ,		image=_a      ,		strength=0.7_5      ,		guidance_scale=7.5      ,		num_inference_steps=20      ,		generator=_a      ,		output_type="""np"""      ,		)
        UpperCamelCase      = output.images
        UpperCamelCase      = images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        UpperCamelCase      = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]				)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 2e-2








'''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
