






from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=1_0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=None  ,      ):
         UpperCamelCase_: str   				=    parent
         UpperCamelCase_: str   				=    batch_size
         UpperCamelCase_: int   				=    image_size
         UpperCamelCase_: Any   				=    patch_size
         UpperCamelCase_: Dict   				=    num_channels
         UpperCamelCase_: Dict   				=    is_training
         UpperCamelCase_: Any   				=    use_labels
         UpperCamelCase_: Optional[int]   				=    hidden_size
         UpperCamelCase_: str   				=    num_hidden_layers
         UpperCamelCase_: int   				=    num_attention_heads
         UpperCamelCase_: Optional[int]   				=    intermediate_size
         UpperCamelCase_: List[str]   				=    hidden_act
         UpperCamelCase_: Optional[Any]   				=    hidden_dropout_prob
         UpperCamelCase_: List[str]   				=    attention_probs_dropout_prob
         UpperCamelCase_: Tuple   				=    type_sequence_label_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    scope

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCamelCase_: Tuple   				=    (image_size // patch_size) ** 2
         UpperCamelCase_: List[str]   				=    num_patches + 1





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCamelCase_: List[Any]   				=    None
         if self.use_labels:
           UpperCamelCase_: str   				=    ids_tensor([self.batch_size]  ,      self.type_sequence_label_size							)

         UpperCamelCase_: List[Any]   				=    self.get_config()

         return config, pixel_values, labels





       def       _a   ( self							):
         return ViTConfig(
             image_size=self.image_size  ,      patch_size=self.patch_size  ,      num_channels=self.num_channels  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      is_decoder=_lowerCamelCase  ,      initializer_range=self.initializer_range  ,      )





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    TFViTModel(config=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)							)

         # Test with an image with different size than the one specified in config.
         UpperCamelCase_: int   				=    self.image_size // 2
         UpperCamelCase_: List[str]   				=    pixel_values[:, :, :image_size, :image_size]
         UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      interpolate_pos_encoding=_lowerCamelCase  ,      training=_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    (image_size // self.patch_size) ** 2 + 1
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, seq_length, self.hidden_size)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Dict   				=    self.type_sequence_label_size
         UpperCamelCase_: Tuple   				=    TFViTForImageClassification(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    model(_lowerCamelCase  ,      labels=_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)

         # Test with an image with different size than the one specified in config.
         UpperCamelCase_: List[Any]   				=    self.image_size // 2
         UpperCamelCase_: Union[str, Any]   				=    pixel_values[:, :, :image_size, :image_size]
         UpperCamelCase_: int   				=    model(_lowerCamelCase  ,      interpolate_pos_encoding=_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         UpperCamelCase_: Optional[int]   				=    1
         UpperCamelCase_: Tuple   				=    TFViTForImageClassification(_lowerCamelCase							)
         UpperCamelCase_: str   				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         UpperCamelCase_: int   				=    model(_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.prepare_config_and_inputs()
         UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    config_and_inputs
         UpperCamelCase_: int   				=    {'pixel_values': pixel_values}
         return config, inputs_dict







@require_tf
class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		List[str]									=(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
       a    :		Tuple									=(
           {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
           if is_tf_available()
           else {}
       )

       a    :		Tuple									=False
       a    :		int									=False
       a    :		int									=False





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    TFViTModelTester(self							)
         UpperCamelCase_: List[str]   				=    ConfigTester(self  ,      config_class=_lowerCamelCase  ,      has_text_modality=_lowerCamelCase  ,      hidden_size=3_7							)





       def       _a   ( self							):
         self.config_tester.run_common_tests()





       @unittest.skip(reason='ViT does not use inputs_embeds'							)
       def       _a   ( self							):
         pass





       @unittest.skip(reason='ViT does not use inputs_embeds'							)
       def       _a   ( self							):
         pass





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           UpperCamelCase_: Optional[int]   				=    model_class(_lowerCamelCase							)
           self.assertIsInstance(model.get_input_embeddings()  ,      (tf.keras.layers.Layer)							)
           UpperCamelCase_: Union[str, Any]   				=    model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_lowerCamelCase  ,      tf.keras.layers.Layer							)							)





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           UpperCamelCase_: Optional[int]   				=    model_class(_lowerCamelCase							)
           UpperCamelCase_: Optional[int]   				=    inspect.signature(model.call							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase_: List[str]   				=    [*signature.parameters.keys()]

           UpperCamelCase_: List[str]   				=    ['pixel_values']
           self.assertListEqual(arg_names[:1]  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase							)





       @slow
       def       _a   ( self							):
         UpperCamelCase_: int   				=    TFViTModel.from_pretrained('google/vit-base-patch16-224'							)
         self.assertIsNotNone(_lowerCamelCase							)







def   snake_case ()	->    Optional[int]:
  UpperCamelCase_: Any   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image




@require_tf
@require_vision
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @cached_property
       def       _a   ( self							):
         return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'							) if is_vision_available() else None





       @slow
       def       _a   ( self							):
         UpperCamelCase_: str   				=    TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'							)

         UpperCamelCase_: str   				=    self.default_image_processor
         UpperCamelCase_: Any   				=    prepare_img()
         UpperCamelCase_: Optional[Any]   				=    image_processor(images=_lowerCamelCase  ,      return_tensors='tf'							)

         # forward pass
         UpperCamelCase_: Union[str, Any]   				=    model(**_lowerCamelCase							)

         # verify the logits
         UpperCamelCase_: List[Any]   				=    tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]							)

         tf.debugging.assert_near(outputs.logits[0, :3]  ,      _lowerCamelCase  ,      atol=1e-4							)







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   snake_case (UpperCAmelCase__  )	->    Optional[int]:  # picklable for multiprocessing
  return x.sum()

def   snake_case (UpperCAmelCase__  )	->    Any:  # picklable for multiprocessing
  return i + 1




@dataclass
class     _lowerCAmelCase:





       """simple docstring"""







       a    :		int
       a    :		str







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    {}
         UpperCamelCase_: List[str]   				=    []
         UpperCamelCase_: Any   				=    1
         UpperCamelCase_: Optional[int]   				=    [1, 2]
         UpperCamelCase_: List[str]   				=    {'a': 1, 'b': 2}
         UpperCamelCase_: Tuple   				=    {'a': [1, 2], 'b': [3, 4]}
         UpperCamelCase_: Optional[int]   				=    {'a': {'1': 1}, 'b': 2}
         UpperCamelCase_: Optional[Any]   				=    {'a': 1, 'b': 2, 'c': 3, 'd': 4}
         UpperCamelCase_: Tuple   				=    {}
         UpperCamelCase_: str   				=    []
         UpperCamelCase_: List[Any]   				=    2
         UpperCamelCase_: List[Any]   				=    [2, 3]
         UpperCamelCase_: Optional[Any]   				=    {'a': 2, 'b': 3}
         UpperCamelCase_: List[str]   				=    {'a': [2, 3], 'b': [4, 5]}
         UpperCamelCase_: Any   				=    {'a': {'1': 2}, 'b': 3}
         UpperCamelCase_: List[str]   				=    {'a': 2, 'b': 3, 'c': 4, 'd': 5}
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase							)  ,      _lowerCamelCase							)

         UpperCamelCase_: Optional[int]   				=    2
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)
         self.assertEqual(map_nested(_lowerCamelCase  ,      _lowerCamelCase  ,      num_proc=_lowerCamelCase							)  ,      _lowerCamelCase							)

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





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





       def       _a   ( self							):




         class     _lowerCAmelCase:





                """simple docstring"""







                a    :		str									='''bar'''







         UpperCamelCase_: int   				=    Foo()
         self.assertEqual(foo.my_attr  ,      'bar'							)
         with temporary_assignment(_lowerCamelCase  ,      '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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]     ,   )
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  with patch('datasets.utils.py_utils._single_map_nested'  ) as mock_single_map_nested, patch(
      'datasets.parallel.parallel.Pool'  ) as mock_multiprocessing_pool:
    UpperCamelCase_: Any   				=    {F'''{i}''': i for i in range(UpperCAmelCase__  )}
    UpperCamelCase_: int   				=    map_nested(lambda UpperCAmelCase__  : x + 1_0     ,   UpperCAmelCase__     ,   num_proc=UpperCAmelCase__     ,   parallel_min_length=1_6  )
    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(	UpperCAmelCase_ ):





       """simple docstring"""







       @require_tf
       def       _a   ( self							):
         import tensorflow as tf
         from tensorflow.keras import layers

         UpperCamelCase_: Dict   				=    layers.Dense(2							)

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

         with temp_seed(4_2  ,      set_tensorflow=_lowerCamelCase							):
           UpperCamelCase_: int   				=    gen_random_output()
         with temp_seed(4_2  ,      set_tensorflow=_lowerCamelCase							):
           UpperCamelCase_: List[str]   				=    gen_random_output()
         UpperCamelCase_: str   				=    gen_random_output()

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





       @require_torch
       def       _a   ( self							):
         import torch

         def gen_random_output():
           UpperCamelCase_: Any   				=    torch.nn.Linear(3  ,      2							)
           UpperCamelCase_: Optional[Any]   				=    torch.rand(1  ,      3							)
           return model(_lowerCamelCase							).detach().numpy()

         with temp_seed(4_2  ,      set_pytorch=_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    gen_random_output()
         with temp_seed(4_2  ,      set_pytorch=_lowerCamelCase							):
           UpperCamelCase_: str   				=    gen_random_output()
         UpperCamelCase_: str   				=    gen_random_output()

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





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

         with temp_seed(4_2							):
           UpperCamelCase_: Optional[Any]   				=    gen_random_output()
         with temp_seed(4_2							):
           UpperCamelCase_: Tuple   				=    gen_random_output()
         UpperCamelCase_: Optional[int]   				=    gen_random_output()

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







@pytest.mark.parametrize('input_data'     ,   [{}]  )
def   snake_case (UpperCAmelCase__  )	->    Dict:
  UpperCamelCase_: str   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  UpperCamelCase_: Optional[Any]   				=    NestedDataStructure(UpperCAmelCase__  ).flatten()
  assert output == expected_output

def   snake_case ()	->    Optional[int]:
  UpperCamelCase_: List[Any]   				=    A(x=1     ,   y='foobar'  )
  UpperCamelCase_: Optional[int]   				=    {'x': 1, 'y': 'foobar'}
  assert asdict(UpperCAmelCase__  ) == expected_output

  UpperCamelCase_: List[str]   				=    {'a': {'b': A(x=1_0     ,   y='foo'  )}, 'c': [A(x=2_0     ,   y='bar'  )]}
  UpperCamelCase_: Tuple   				=    {'a': {'b': {'x': 1_0, 'y': 'foo'}}, 'c': [{'x': 2_0, 'y': 'bar'}]}
  assert asdict(UpperCAmelCase__  ) == expected_output

  with pytest.raises(UpperCAmelCase__  ):
    asdict([1, A(x=1_0     ,   y='foo'  )]  )

def   snake_case (UpperCAmelCase__  )	->    Optional[Any]:
  return text.split()

def   snake_case (UpperCAmelCase__  )	->    str:
  yield (time.time(), content)
  time.sleep(2  )
  yield (time.time(), content)

def   snake_case ()	->    Union[str, Any]:
  with Pool(2  ) as pool:
    UpperCamelCase_: Optional[Any]   				=    list(iflatmap_unordered(UpperCAmelCase__     ,   _split_text     ,   kwargs_iterable=[{'text': 'hello there'}] * 1_0  )  )
    assert out.count('hello'  ) == 1_0
    assert out.count('there'  ) == 1_0
    assert len(UpperCAmelCase__  ) == 2_0

  # check multiprocess from pathos (uses dill for pickling)
  with multiprocess.Pool(2  ) as pool:
    UpperCamelCase_: Optional[int]   				=    list(iflatmap_unordered(UpperCAmelCase__     ,   _split_text     ,   kwargs_iterable=[{'text': 'hello there'}] * 1_0  )  )
    assert out.count('hello'  ) == 1_0
    assert out.count('there'  ) == 1_0
    assert len(UpperCAmelCase__  ) == 2_0

  # check that we get items as fast as possible
  with Pool(2  ) as pool:
    UpperCamelCase_: Any   				=    []
    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