





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 DetrImageProcessor







class 			__A       (      unittest.TestCase  ):




   """simple docstring"""







   def __init__(		self      ,							a__      ,							a__=7      ,							a__=3      ,							a__=30      ,							a__=400      ,							a__=True      ,							a__=None      ,							a__=True      ,							a__=1 / 255      ,							a__=True      ,							a__=[0.5, 0.5, 0.5]      ,							a__=[0.5, 0.5, 0.5]      ,							a__=True      ,							):



          """simple docstring"""





          _lowerCamelCase							:     Dict	=		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          _lowerCamelCase							:     Tuple	=		parent
          _lowerCamelCase							:     int	=		batch_size
          _lowerCamelCase							:     Optional[int]	=		num_channels
          _lowerCamelCase							:     Dict	=		min_resolution
          _lowerCamelCase							:     str	=		max_resolution
          _lowerCamelCase							:     Dict	=		do_resize
          _lowerCamelCase							:     Optional[Any]	=		size
          _lowerCamelCase							:     Optional[int]	=		do_rescale
          _lowerCamelCase							:     Any	=		rescale_factor
          _lowerCamelCase							:     List[Any]	=		do_normalize
          _lowerCamelCase							:     Optional[int]	=		image_mean
          _lowerCamelCase							:     Any	=		image_std
          _lowerCamelCase							:     List[str]	=		do_pad


   def 				__snake_case			(		self):



          """simple docstring"""





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



   def 				__snake_case			(		self      ,							a__      ,							a__=False):



          """simple docstring"""





          if not batched:
                 _lowerCamelCase							:     Optional[Any]	=		image_inputs[0]
                 if isinstance(a__      ,							Image.Image):
                        _lowerCamelCase							:     Optional[int]	=		image.size
                 else:
                        _lowerCamelCase							:     Any	=		image.shape[1], image.shape[2]
                 if w < h:
                        _lowerCamelCase							:     List[Any]	=		int(self.size['''shortest_edge'''] * h / w)
                        _lowerCamelCase							:     Optional[int]	=		self.size['''shortest_edge''']
                 elif w > h:
                        _lowerCamelCase							:     List[str]	=		self.size['''shortest_edge''']
                        _lowerCamelCase							:     Tuple	=		int(self.size['''shortest_edge'''] * w / h)
                 else:
                        _lowerCamelCase							:     List[Any]	=		self.size['''shortest_edge''']
                        _lowerCamelCase							:     str	=		self.size['''shortest_edge''']

          else:
                 _lowerCamelCase							:     List[Any]	=		[]
                 for image in image_inputs:
                        _lowerCamelCase							:     List[Any]	=		self.get_expected_values([image])
                        expected_values.append((expected_height, expected_width))
                 _lowerCamelCase							:     Any	=		max(a__      ,							key=lambda a__: item[0])[0]
                 _lowerCamelCase							:     int	=		max(a__      ,							key=lambda a__: item[1])[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 			__A       (      lowerCamelCase__ ,unittest.TestCase  ):




   """simple docstring"""







   UpperCAmelCase__						=      DetrImageProcessor if is_vision_available() else None


   def 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     Union[str, Any]	=		DetrImageProcessingTester(self)


   @property
   def 				__snake_case			(		self):



          """simple docstring"""





          return self.image_processor_tester.prepare_image_processor_dict()


   def 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     Union[str, Any]	=		self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(a__      ,							'''image_mean'''))
          self.assertTrue(hasattr(a__      ,							'''image_std'''))
          self.assertTrue(hasattr(a__      ,							'''do_normalize'''))
          self.assertTrue(hasattr(a__      ,							'''do_rescale'''))
          self.assertTrue(hasattr(a__      ,							'''rescale_factor'''))
          self.assertTrue(hasattr(a__      ,							'''do_resize'''))
          self.assertTrue(hasattr(a__      ,							'''size'''))
          self.assertTrue(hasattr(a__      ,							'''do_pad'''))


   def 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     List[str]	=		self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size      ,							{'''shortest_edge''': 18, '''longest_edge''': 1333})
          self.assertEqual(image_processor.do_pad      ,							a__)

          _lowerCamelCase							:     int	=		self.image_processing_class.from_dict(
              self.image_processor_dict      ,							size=42      ,							max_size=84      ,							pad_and_return_pixel_mask=a__)
          self.assertEqual(image_processor.size      ,							{'''shortest_edge''': 42, '''longest_edge''': 84})
          self.assertEqual(image_processor.do_pad      ,							a__)


   def 				__snake_case			(		self):



          """simple docstring"""





          pass


   def 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     Optional[Any]	=		self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowerCamelCase							:     List[str]	=		prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=a__)
          for image in image_inputs:
                 self.assertIsInstance(a__      ,							Image.Image)

          # Test not batched input
          _lowerCamelCase							:     str	=		image_processing(image_inputs[0]      ,							return_tensors='''pt''').pixel_values

          _lowerCamelCase							:     Tuple	=		self.image_processor_tester.get_expected_values(a__)

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

          # Test batched
          _lowerCamelCase							:     Tuple	=		self.image_processor_tester.get_expected_values(a__      ,							batched=a__)

          _lowerCamelCase							:     List[Any]	=		image_processing(a__      ,							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 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     Union[str, Any]	=		self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase							:     List[Any]	=		prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=a__      ,							numpify=a__)
          for image in image_inputs:
                 self.assertIsInstance(a__      ,							np.ndarray)

          # Test not batched input
          _lowerCamelCase							:     Tuple	=		image_processing(image_inputs[0]      ,							return_tensors='''pt''').pixel_values

          _lowerCamelCase							:     int	=		self.image_processor_tester.get_expected_values(a__)

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

          # Test batched
          _lowerCamelCase							:     Dict	=		image_processing(a__      ,							return_tensors='''pt''').pixel_values

          _lowerCamelCase							:     List[str]	=		self.image_processor_tester.get_expected_values(a__      ,							batched=a__)

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


   def 				__snake_case			(		self):



          """simple docstring"""





          _lowerCamelCase							:     Optional[Any]	=		self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowerCamelCase							:     Optional[Any]	=		prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=a__      ,							torchify=a__)
          for image in image_inputs:
                 self.assertIsInstance(a__      ,							torch.Tensor)

          # Test not batched input
          _lowerCamelCase							:     Dict	=		image_processing(image_inputs[0]      ,							return_tensors='''pt''').pixel_values

          _lowerCamelCase							:     Optional[int]	=		self.image_processor_tester.get_expected_values(a__)

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

          # Test batched
          _lowerCamelCase							:     Tuple	=		image_processing(a__      ,							return_tensors='''pt''').pixel_values

          _lowerCamelCase							:     List[str]	=		self.image_processor_tester.get_expected_values(a__      ,							batched=a__)

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


   @slow
   def 				__snake_case			(		self):



          """simple docstring"""





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

          _lowerCamelCase							:     Optional[Any]	=		{'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          _lowerCamelCase							:     Optional[int]	=		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''')
          _lowerCamelCase							:     Union[str, Any]	=		image_processing(images=a__      ,							annotations=a__      ,							return_tensors='''pt''')

          # verify pixel values
          _lowerCamelCase							:     Dict	=		torch.Size([1, 3, 800, 1066])
          self.assertEqual(encoding['''pixel_values'''].shape      ,							a__)

          _lowerCamelCase							:     List[Any]	=		torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,							a__      ,							atol=1e-4))

          # verify area
          _lowerCamelCase							:     List[Any]	=		torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,							a__))
          # verify boxes
          _lowerCamelCase							:     Optional[Any]	=		torch.Size([6, 4])
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,							a__)
          _lowerCamelCase							:     Any	=		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,							a__      ,							atol=1e-3))
          # verify image_id
          _lowerCamelCase							:     List[Any]	=		torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,							a__))
          # verify is_crowd
          _lowerCamelCase							:     Tuple	=		torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,							a__))
          # verify class_labels
          _lowerCamelCase							:     Optional[Any]	=		torch.tensor([75, 75, 63, 65, 17, 17])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,							a__))
          # verify orig_size
          _lowerCamelCase							:     Union[str, Any]	=		torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,							a__))
          # verify size
          _lowerCamelCase							:     List[Any]	=		torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,							a__))



   @slow
   def 				__snake_case			(		self):



          """simple docstring"""





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

          _lowerCamelCase							:     int	=		{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

          _lowerCamelCase							:     List[str]	=		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

          # encode them
          _lowerCamelCase							:     Optional[int]	=		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''')
          _lowerCamelCase							:     Tuple	=		image_processing(images=a__      ,							annotations=a__      ,							masks_path=a__      ,							return_tensors='''pt''')

          # verify pixel values
          _lowerCamelCase							:     List[Any]	=		torch.Size([1, 3, 800, 1066])
          self.assertEqual(encoding['''pixel_values'''].shape      ,							a__)

          _lowerCamelCase							:     int	=		torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,							a__      ,							atol=1e-4))

          # verify area
          _lowerCamelCase							:     Dict	=		torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,							a__))
          # verify boxes
          _lowerCamelCase							:     Dict	=		torch.Size([6, 4])
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,							a__)
          _lowerCamelCase							:     str	=		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,							a__      ,							atol=1e-3))
          # verify image_id
          _lowerCamelCase							:     int	=		torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,							a__))
          # verify is_crowd
          _lowerCamelCase							:     Union[str, Any]	=		torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,							a__))
          # verify class_labels
          _lowerCamelCase							:     Any	=		torch.tensor([17, 17, 63, 75, 75, 93])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,							a__))
          # verify masks
          _lowerCamelCase							:     List[Any]	=		82_2873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()      ,							a__)
          # verify orig_size
          _lowerCamelCase							:     Any	=		torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,							a__))
          # verify size
          _lowerCamelCase							:     int	=		torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,							a__))








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    __UpperCAmelCase(      lowercase_      ):  # picklable for multiprocessing
       return x.sum()




def    __UpperCAmelCase(      lowercase_      ):  # picklable for multiprocessing
       return i + 1







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		{}
             _lowerCamelCase							:     Dict	=		[]
             _lowerCamelCase							:     Optional[Any]	=		1
             _lowerCamelCase							:     Optional[int]	=		[1, 2]
             _lowerCamelCase							:     str	=		{'''a''': 1, '''b''': 2}
             _lowerCamelCase							:     Dict	=		{'''a''': [1, 2], '''b''': [3, 4]}
             _lowerCamelCase							:     Any	=		{'''a''': {'''1''': 1}, '''b''': 2}
             _lowerCamelCase							:     Optional[Any]	=		{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
             _lowerCamelCase							:     str	=		{}
             _lowerCamelCase							:     int	=		[]
             _lowerCamelCase							:     str	=		2
             _lowerCamelCase							:     int	=		[2, 3]
             _lowerCamelCase							:     str	=		{'''a''': 2, '''b''': 3}
             _lowerCamelCase							:     Tuple	=		{'''a''': [2, 3], '''b''': [4, 5]}
             _lowerCamelCase							:     List[str]	=		{'''a''': {'''1''': 2}, '''b''': 3}
             _lowerCamelCase							:     str	=		{'''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__)

             _lowerCamelCase							:     Dict	=		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__)

             _lowerCamelCase							:     Any	=		{'''a''': np.eye(2), '''b''': np.zeros(3), '''c''': np.ones(2)}
             _lowerCamelCase							:     Optional[int]	=		{'''a''': 2, '''b''': 0, '''c''': 2}
             _lowerCamelCase							:     Optional[int]	=		{
                 '''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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		{'''a''': 1, '''b''': 2}
             _lowerCamelCase							:     Optional[int]	=		{'''a''': 3, '''b''': 4}
             _lowerCamelCase							:     int	=		{'''a''': 5, '''b''': 6}
             _lowerCamelCase							:     Optional[int]	=		sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
             self.assertEqual(sorted(zip_dict(a__      ,							a__      ,							a__))      ,							a__)



      def 				__snake_case			(		self):



             """simple docstring"""





             class 			__A       :




                   """simple docstring"""







                   UpperCAmelCase__						=      """bar"""
             _lowerCamelCase							:     Any	=		Foo()
             self.assertEqual(foo.my_attr      ,							'''bar''')
             with temporary_assignment(a__      ,							'''my_attr'''      ,							'''BAR'''):
                    self.assertEqual(foo.my_attr      ,							'''BAR''')
             self.assertEqual(foo.my_attr      ,							'''bar''')




@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc''' ,    [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] ,    )
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       with patch('''datasets.utils.py_utils._single_map_nested'''      ) as mock_single_map_nested, patch(
           '''datasets.parallel.parallel.Pool'''      ) as mock_multiprocessing_pool:
              _lowerCamelCase							:     Union[str, Any]	=		{F"""{i}""": i for i in range(lowercase_      )}
              _lowerCamelCase							:     List[str]	=		map_nested(lambda lowercase_      : x + 10 ,    lowercase_ ,    num_proc=lowercase_ ,    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__  ):




      """simple docstring"""







      @require_tf
      def 				__snake_case			(		self):



             """simple docstring"""





             import tensorflow as tf
             from tensorflow.keras import layers

             _lowerCamelCase							:     int	=		layers.Dense(2)

             def gen_random_output():
                    _lowerCamelCase							:     Union[str, Any]	=		tf.random.uniform((1, 3))
                    return model(a__).numpy()

             with temp_seed(42      ,							set_tensorflow=a__):
                    _lowerCamelCase							:     List[str]	=		gen_random_output()
             with temp_seed(42      ,							set_tensorflow=a__):
                    _lowerCamelCase							:     Any	=		gen_random_output()
             _lowerCamelCase							:     str	=		gen_random_output()

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


      @require_torch
      def 				__snake_case			(		self):



             """simple docstring"""





             import torch

             def gen_random_output():
                    _lowerCamelCase							:     Union[str, Any]	=		torch.nn.Linear(3      ,							2)
                    _lowerCamelCase							:     Dict	=		torch.rand(1      ,							3)
                    return model(a__).detach().numpy()

             with temp_seed(42      ,							set_pytorch=a__):
                    _lowerCamelCase							:     Any	=		gen_random_output()
             with temp_seed(42      ,							set_pytorch=a__):
                    _lowerCamelCase							:     Optional[int]	=		gen_random_output()
             _lowerCamelCase							:     Union[str, Any]	=		gen_random_output()

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



      def 				__snake_case			(		self):



             """simple docstring"""





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

             with temp_seed(42):
                    _lowerCamelCase							:     Union[str, Any]	=		gen_random_output()
             with temp_seed(42):
                    _lowerCamelCase							:     List[str]	=		gen_random_output()
             _lowerCamelCase							:     str	=		gen_random_output()

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




@pytest.mark.parametrize('''input_data''' ,    [{}]      )
def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     List[Any]	=		NestedDataStructure(lowercase_      ).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    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		NestedDataStructure(lowercase_      ).flatten()
       assert output == expected_output




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Any	=		A(x=1 ,    y='''foobar'''      )
       _lowerCamelCase							:     Union[str, Any]	=		{'''x''': 1, '''y''': '''foobar'''}
       assert asdict(lowercase_      ) == expected_output

       _lowerCamelCase							:     Optional[int]	=		{'''a''': {'''b''': A(x=10 ,    y='''foo'''      )}, '''c''': [A(x=20 ,    y='''bar'''      )]}
       _lowerCamelCase							:     Union[str, Any]	=		{'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
       assert asdict(lowercase_      ) == expected_output

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




def    __UpperCAmelCase(      lowercase_      ):
       return text.split()




def    __UpperCAmelCase(      lowercase_      ):
       yield (time.time(), content)
       time.sleep(2      )
       yield (time.time(), content)




def    __UpperCAmelCase(      ):
       with Pool(2      ) as pool:
              _lowerCamelCase							:     Tuple	=		list(iflatmap_unordered(lowercase_ ,    _split_text ,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10      )      )
              assert out.count('''hello'''      ) == 10
              assert out.count('''there'''      ) == 10
              assert len(lowercase_      ) == 20

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

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

