





import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case           =			logging.get_logger(__name__)


def _A       (   __magic_name__	):
   print("Loading config file..."	)

   def flatten_yaml_as_dict(__magic_name__  , __magic_name__=""  , __magic_name__="."	):
      lowercase__			       =  []
      for k, v in d.items():
         lowercase__			       =  parent_key + sep + k if parent_key else k
         if isinstance(__magic_name__  , collections.abc.MutableMapping	):
            items.extend(flatten_yaml_as_dict(__magic_name__  , __magic_name__  , sep=__magic_name__	).items()	)
         else:
            items.append((new_key, v)	)
      return dict(__magic_name__	)

   lowercase__			       =  argparse.Namespace()
   with open(__magic_name__  , "r"	) as yaml_file:
      try:
         lowercase__			       =  yaml.load(__magic_name__  , Loader=yaml.FullLoader	)

         lowercase__			       =  flatten_yaml_as_dict(__magic_name__	)
         for k, v in flat_cfg.items():
            setattr(__magic_name__  , __magic_name__  , __magic_name__	)
      except yaml.YAMLError as exc:
         logger.error("Error while loading config file: {}. Error message: {}".format(__magic_name__  , str(__magic_name__	)	)	)
   return config


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  MobileViTVaConfig()

   lowercase__			       =  False

   # dataset
   if task_name.startswith("imagenet1k_"	):
      lowercase__			       =  1000
      if int(task_name.strip().split("_"	)[-1]	) == 384:
         lowercase__			       =  384
      else:
         lowercase__			       =  256
      lowercase__			       =  "imagenet-1k-id2label.json"
   elif task_name.startswith("imagenet21k_to_1k_"	):
      lowercase__			       =  2_1000
      if int(task_name.strip().split("_"	)[-1]	) == 384:
         lowercase__			       =  384
      else:
         lowercase__			       =  256
      lowercase__			       =  "imagenet-22k-id2label.json"
   elif task_name.startswith("ade20k_"	):
      lowercase__			       =  151
      lowercase__			       =  512
      lowercase__			       =  "ade20k-id2label.json"
      lowercase__			       =  True
   elif task_name.startswith("voc_"	):
      lowercase__			       =  21
      lowercase__			       =  512
      lowercase__			       =  "pascal-voc-id2label.json"
      lowercase__			       =  True

   # orig_config
   lowercase__			       =  load_orig_config_file(__magic_name__	)
   assert getattr(__magic_name__  , "model.classification.name"  , -1	) == "mobilevit_v2", "Invalid model"
   lowercase__			       =  getattr(__magic_name__  , "model.classification.mitv2.width_multiplier"  , 1.0	)
   assert (
       getattr(__magic_name__  , "model.classification.mitv2.attn_norm_layer"  , -1	) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   lowercase__			       =  getattr(__magic_name__  , "model.classification.activation.name"  , "swish"	)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      lowercase__			       =  getattr(__magic_name__  , "model.segmentation.output_stride"  , 16	)
      if "_deeplabv3" in task_name:
         lowercase__			       =  getattr(__magic_name__  , "model.segmentation.deeplabv3.aspp_rates"  , [12, 24, 36]	)
         lowercase__			       =  getattr(__magic_name__  , "model.segmentation.deeplabv3.aspp_out_channels"  , 512	)
         lowercase__			       =  getattr(__magic_name__  , "model.segmentation.deeplabv3.aspp_dropout"  , 0.1	)

    # id2label
   lowercase__			       =  "huggingface/label-files"
   lowercase__			       =  json.load(open(hf_hub_download(__magic_name__  , __magic_name__  , repo_type="dataset"	)  , "r"	)	)
   lowercase__			       =  {int(__magic_name__	): v for k, v in idalabel.items()}
   lowercase__			       =  idalabel
   lowercase__			       =  {v: k for k, v in idalabel.items()}

   return config


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  dct.pop(__magic_name__	)
   lowercase__			       =  val


def _A       (   __magic_name__  , __magic_name__=False	):
   if base_model:
      lowercase__			       =  ""
   else:
      lowercase__			       =  "mobilevitv2."

   lowercase__			       =  []
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         lowercase__			       =  k[8:]
      else:
         lowercase__			       =  k

      if ".block." in k:
         lowercase__			       =  k_new.replace(".block."  , "."	)
      if ".conv." in k:
         lowercase__			       =  k_new.replace(".conv."  , ".convolution."	)
      if ".norm." in k:
         lowercase__			       =  k_new.replace(".norm."  , ".normalization."	)

      if "conv_1." in k:
         lowercase__			       =  k_new.replace("conv_1."  , f'''{model_prefix}conv_stem.'''	)
      for i in [1, 2]:
         if f'''layer_{i}.''' in k:
            lowercase__			       =  k_new.replace(f'''layer_{i}.'''  , f'''{model_prefix}encoder.layer.{i-1}.layer.'''	)
      if ".exp_1x1." in k:
         lowercase__			       =  k_new.replace(".exp_1x1."  , ".expand_1x1."	)
      if ".red_1x1." in k:
         lowercase__			       =  k_new.replace(".red_1x1."  , ".reduce_1x1."	)

      for i in [3, 4, 5]:
         if f'''layer_{i}.0.''' in k:
            lowercase__			       =  k_new.replace(f'''layer_{i}.0.'''  , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''	)
         if f'''layer_{i}.1.local_rep.0.''' in k:
            lowercase__			       =  k_new.replace(f'''layer_{i}.1.local_rep.0.'''  , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''	)
         if f'''layer_{i}.1.local_rep.1.''' in k:
            lowercase__			       =  k_new.replace(f'''layer_{i}.1.local_rep.1.'''  , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''	)

      for i in [3, 4, 5]:
         if i == 3:
            lowercase__			       =  [0, 1]
         elif i == 4:
            lowercase__			       =  [0, 1, 2, 3]
         elif i == 5:
            lowercase__			       =  [0, 1, 2]

         for j in j_in:
            if f'''layer_{i}.1.global_rep.{j}.''' in k:
               lowercase__			       =  k_new.replace(
                   f'''layer_{i}.1.global_rep.{j}.'''  , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''	)
         if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
            lowercase__			       =  k_new.replace(
                f'''layer_{i}.1.global_rep.{j+1}.'''  , f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''	)

         if f'''layer_{i}.1.conv_proj.''' in k:
            lowercase__			       =  k_new.replace(f'''layer_{i}.1.conv_proj.'''  , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''	)

      if "pre_norm_attn.0." in k:
         lowercase__			       =  k_new.replace("pre_norm_attn.0."  , "layernorm_before."	)
      if "pre_norm_attn.1." in k:
         lowercase__			       =  k_new.replace("pre_norm_attn.1."  , "attention."	)
      if "pre_norm_ffn.0." in k:
         lowercase__			       =  k_new.replace("pre_norm_ffn.0."  , "layernorm_after."	)
      if "pre_norm_ffn.1." in k:
         lowercase__			       =  k_new.replace("pre_norm_ffn.1."  , "ffn.conv1."	)
      if "pre_norm_ffn.3." in k:
         lowercase__			       =  k_new.replace("pre_norm_ffn.3."  , "ffn.conv2."	)

      if "classifier.1." in k:
         lowercase__			       =  k_new.replace("classifier.1."  , "classifier."	)

      if "seg_head." in k:
         lowercase__			       =  k_new.replace("seg_head."  , "segmentation_head."	)
      if ".aspp_layer." in k:
         lowercase__			       =  k_new.replace(".aspp_layer."  , "."	)
      if ".aspp_pool." in k:
         lowercase__			       =  k_new.replace(".aspp_pool."  , "."	)

      rename_keys.append((k, k_new)	)
   return rename_keys


def _A       (   __magic_name__	):
   lowercase__			       =  []
   for k in state_dict.keys():
      if k.startswith("seg_head.aux_head."	):
         keys_to_ignore.append(__magic_name__	)
   for k in keys_to_ignore:
      state_dict.pop(__magic_name__  , __magic_name__	)


def _A       (   ):
   lowercase__			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   lowercase__			       =  Image.open(requests.get(__magic_name__  , stream=__magic_name__	).raw	)
   return im


@torch.no_grad()
def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  get_mobilevitva_config(__magic_name__  , __magic_name__	)

   # load original state_dict
   lowercase__			       =  torch.load(__magic_name__  , map_location="cpu"	)

   # load huggingface model
   if task_name.startswith("ade20k_"	) or task_name.startswith("voc_"	):
      lowercase__			       =  MobileViTVaForSemanticSegmentation(__magic_name__	).eval()
      lowercase__			       =  False
   else:
      lowercase__			       =  MobileViTVaForImageClassification(__magic_name__	).eval()
      lowercase__			       =  False

   # remove and rename some keys of load the original model
   lowercase__			       =  checkpoint
   remove_unused_keys(__magic_name__	)
   lowercase__			       =  create_rename_keys(__magic_name__  , base_model=__magic_name__	)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__magic_name__  , __magic_name__  , __magic_name__	)

   # load modified state_dict
   model.load_state_dict(__magic_name__	)

   # Check outputs on an image, prepared by MobileViTImageProcessor
   lowercase__			       =  MobileViTImageProcessor(crop_size=config.image_size  , size=config.image_size + 32	)
   lowercase__			       =  image_processor(images=prepare_img()  , return_tensors="pt"	)
   lowercase__			       =  model(**__magic_name__	)

   # verify classification model
   if task_name.startswith("imagenet"	):
      lowercase__			       =  outputs.logits
      lowercase__			       =  logits.argmax(-1	).item()
      print("Predicted class:"  , model.config.idalabel[predicted_class_idx]	)
      if task_name.startswith("imagenet1k_256"	) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         lowercase__			       =  torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]	)
         assert torch.allclose(logits[0, :3]  , __magic_name__  , atol=1e-4	)

   Path(__magic_name__	).mkdir(exist_ok=__magic_name__	)
   print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''	)
   model.save_pretrained(__magic_name__	)
   print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
   image_processor.save_pretrained(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--task""",
      default="""imagenet1k_256""",
      type=str,
      help=(
          """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
          """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
      ),
      choices=[
          """imagenet1k_256""",
          """imagenet1k_384""",
          """imagenet21k_to_1k_256""",
          """imagenet21k_to_1k_384""",
          """ade20k_deeplabv3""",
          """voc_deeplabv3""",
      ],
  )

  parser.add_argument(
      """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
  )
  parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
  )

  _snake_case           =			parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )













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 _A       (   __magic_name__	):  # picklable for multiprocessing
   return x.sum()


def _A       (   __magic_name__	):  # picklable for multiprocessing
   return i + 1





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42





class  lowerCAmelCase							(       lowercase_ ):







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





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

     lowercase__			       =  2
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )
     self.assertEqual(map_nested(_lowercase   ,							_lowercase   ,							num_proc=_lowercase )   ,							_lowercase )

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







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     class  lowerCAmelCase							:
       __lowerCamelCase		=      'bar'
     lowercase__			       =  Foo()
     self.assertEqual(foo.my_attr   ,							"bar" )
     with temporary_assignment(_lowercase   ,							"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 _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   with patch("datasets.utils.py_utils._single_map_nested"	) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"	) as mock_multiprocessing_pool:
      lowercase__			       =  {f'''{i}''': i for i in range(__magic_name__	)}
      lowercase__			       =  map_nested(lambda __magic_name__	: x + 10  , __magic_name__  , num_proc=__magic_name__  , parallel_min_length=16	)
      if expected_num_proc == 1:
         assert mock_single_map_nested.called
         assert not mock_multiprocessing_pool.called
      else:
         assert not mock_single_map_nested.called
         assert mock_multiprocessing_pool.called
         assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class  lowerCAmelCase							(       lowercase_ ):







  @require_tf
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     import tensorflow as tf
     from tensorflow.keras import layers

     lowercase__			       =  layers.Dense(2 )

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

     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_tensorflow=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  @require_torch
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     import torch

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

     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     with temp_seed(42   ,							set_pytorch=_lowercase ):
        lowercase__			       =  gen_random_output()
     lowercase__			       =  gen_random_output()

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







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





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

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

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


@pytest.mark.parametrize("input_data"  , [{}]	)
def _A       (   __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).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 _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  NestedDataStructure(__magic_name__	).flatten()
   assert output == expected_output


def _A       (   ):
   lowercase__			       =  A(x=1  , y="foobar"	)
   lowercase__			       =  {"x": 1, "y": "foobar"}
   assert asdict(__magic_name__	) == expected_output

   lowercase__			       =  {"a": {"b": A(x=10  , y="foo"	)}, "c": [A(x=20  , y="bar"	)]}
   lowercase__			       =  {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
   assert asdict(__magic_name__	) == expected_output

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


def _A       (   __magic_name__	):
   return text.split()


def _A       (   __magic_name__	):
   yield (time.time(), content)
   time.sleep(2	)
   yield (time.time(), content)







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

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

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






