





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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


def _A       (   __magic_name__	):
   lowercase__			       =  DPTConfig(embedding_type="hybrid"	)

   if "large" in checkpoint_url:
      lowercase__			       =  1024
      lowercase__			       =  4096
      lowercase__			       =  24
      lowercase__			       =  16
      lowercase__			       =  [5, 11, 17, 23]
      lowercase__			       =  [256, 512, 1024, 1024]
      lowercase__			       =  (1, 384, 384)

   if "nyu" or "midas" in checkpoint_url:
      lowercase__			       =  768
      lowercase__			       =  [1, 1, 1, 0.5]
      lowercase__			       =  [256, 512, 768, 768]
      lowercase__			       =  150
      lowercase__			       =  16
      lowercase__			       =  (1, 384, 384)
      lowercase__			       =  False
      lowercase__			       =  "project"

   if "ade" in checkpoint_url:
      lowercase__			       =  True
      lowercase__			       =  768
      lowercase__			       =  [1, 1, 1, 0.5]
      lowercase__			       =  150
      lowercase__			       =  16
      lowercase__			       =  "huggingface/label-files"
      lowercase__			       =  "ade20k-id2label.json"
      lowercase__			       =  json.load(open(cached_download(hf_hub_url(lowercase__  , lowercase__  , repo_type="dataset"	)	)  , "r"	)	)
      lowercase__			       =  {int(lowercase__	): v for k, v in idalabel.items()}
      lowercase__			       =  idalabel
      lowercase__			       =  {v: k for k, v in idalabel.items()}
      lowercase__			       =  [1, 150, 480, 480]

   return config, expected_shape


def _A       (   __magic_name__	):
   lowercase__			       =  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
   for k in ignore_keys:
      state_dict.pop(lowercase__  , lowercase__	)


def _A       (   __magic_name__	):
   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      lowercase__			       =  name.replace("pretrained.model"  , "dpt.encoder"	)
   if "pretrained.model" in name:
      lowercase__			       =  name.replace("pretrained.model"  , "dpt.embeddings"	)
   if "patch_embed" in name:
      lowercase__			       =  name.replace("patch_embed"  , ""	)
   if "pos_embed" in name:
      lowercase__			       =  name.replace("pos_embed"  , "position_embeddings"	)
   if "attn.proj" in name:
      lowercase__			       =  name.replace("attn.proj"  , "attention.output.dense"	)
   if "proj" in name and "project" not in name:
      lowercase__			       =  name.replace("proj"  , "projection"	)
   if "blocks" in name:
      lowercase__			       =  name.replace("blocks"  , "layer"	)
   if "mlp.fc1" in name:
      lowercase__			       =  name.replace("mlp.fc1"  , "intermediate.dense"	)
   if "mlp.fc2" in name:
      lowercase__			       =  name.replace("mlp.fc2"  , "output.dense"	)
   if "norm1" in name and "backbone" not in name:
      lowercase__			       =  name.replace("norm1"  , "layernorm_before"	)
   if "norm2" in name and "backbone" not in name:
      lowercase__			       =  name.replace("norm2"  , "layernorm_after"	)
   if "scratch.output_conv" in name:
      lowercase__			       =  name.replace("scratch.output_conv"  , "head"	)
   if "scratch" in name:
      lowercase__			       =  name.replace("scratch"  , "neck"	)
   if "layer1_rn" in name:
      lowercase__			       =  name.replace("layer1_rn"  , "convs.0"	)
   if "layer2_rn" in name:
      lowercase__			       =  name.replace("layer2_rn"  , "convs.1"	)
   if "layer3_rn" in name:
      lowercase__			       =  name.replace("layer3_rn"  , "convs.2"	)
   if "layer4_rn" in name:
      lowercase__			       =  name.replace("layer4_rn"  , "convs.3"	)
   if "refinenet" in name:
      lowercase__			       =  int(name[len("neck.refinenet"	) : len("neck.refinenet"	) + 1]	)
      # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
      lowercase__			       =  name.replace(f'''refinenet{layer_idx}'''  , f'''fusion_stage.layers.{abs(layer_idx-4	)}'''	)
   if "out_conv" in name:
      lowercase__			       =  name.replace("out_conv"  , "projection"	)
   if "resConfUnit1" in name:
      lowercase__			       =  name.replace("resConfUnit1"  , "residual_layer1"	)
   if "resConfUnit2" in name:
      lowercase__			       =  name.replace("resConfUnit2"  , "residual_layer2"	)
   if "conv1" in name:
      lowercase__			       =  name.replace("conv1"  , "convolution1"	)
   if "conv2" in name:
      lowercase__			       =  name.replace("conv2"  , "convolution2"	)
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess1.0.project.0"  , "neck.reassemble_stage.readout_projects.0.0"	)
   if "pretrained.act_postprocess2.0.project.0" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess2.0.project.0"  , "neck.reassemble_stage.readout_projects.1.0"	)
   if "pretrained.act_postprocess3.0.project.0" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess3.0.project.0"  , "neck.reassemble_stage.readout_projects.2.0"	)
   if "pretrained.act_postprocess4.0.project.0" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess4.0.project.0"  , "neck.reassemble_stage.readout_projects.3.0"	)

   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess1.3"  , "neck.reassemble_stage.layers.0.projection"	)
   if "pretrained.act_postprocess1.4" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess1.4"  , "neck.reassemble_stage.layers.0.resize"	)
   if "pretrained.act_postprocess2.3" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess2.3"  , "neck.reassemble_stage.layers.1.projection"	)
   if "pretrained.act_postprocess2.4" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess2.4"  , "neck.reassemble_stage.layers.1.resize"	)
   if "pretrained.act_postprocess3.3" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess3.3"  , "neck.reassemble_stage.layers.2.projection"	)
   if "pretrained.act_postprocess4.3" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess4.3"  , "neck.reassemble_stage.layers.3.projection"	)
   if "pretrained.act_postprocess4.4" in name:
      lowercase__			       =  name.replace("pretrained.act_postprocess4.4"  , "neck.reassemble_stage.layers.3.resize"	)
   if "pretrained" in name:
      lowercase__			       =  name.replace("pretrained"  , "dpt"	)
   if "bn" in name:
      lowercase__			       =  name.replace("bn"  , "batch_norm"	)
   if "head" in name:
      lowercase__			       =  name.replace("head"  , "head.head"	)
   if "encoder.norm" in name:
      lowercase__			       =  name.replace("encoder.norm"  , "layernorm"	)
   if "auxlayer" in name:
      lowercase__			       =  name.replace("auxlayer"  , "auxiliary_head.head"	)
   if "backbone" in name:
      lowercase__			       =  name.replace("backbone"  , "backbone.bit.encoder"	)

   if ".." in name:
      lowercase__			       =  name.replace(".."  , "."	)

   if "stem.conv" in name:
      lowercase__			       =  name.replace("stem.conv"  , "bit.embedder.convolution"	)
   if "blocks" in name:
      lowercase__			       =  name.replace("blocks"  , "layers"	)
   if "convolution" in name and "backbone" in name:
      lowercase__			       =  name.replace("convolution"  , "conv"	)
   if "layer" in name and "backbone" in name:
      lowercase__			       =  name.replace("layer"  , "layers"	)
   if "backbone.bit.encoder.bit" in name:
      lowercase__			       =  name.replace("backbone.bit.encoder.bit"  , "backbone.bit"	)
   if "embedder.conv" in name:
      lowercase__			       =  name.replace("embedder.conv"  , "embedder.convolution"	)
   if "backbone.bit.encoder.stem.norm" in name:
      lowercase__			       =  name.replace("backbone.bit.encoder.stem.norm"  , "backbone.bit.embedder.norm"	)
   return name


def _A       (   __magic_name__  , __magic_name__	):
   for i in range(config.num_hidden_layers	):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      lowercase__			       =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''	)
      lowercase__			       =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''	)
      # next, add query, keys and values (in that order) to the state dict
      lowercase__			       =  in_proj_weight[: config.hidden_size, :]
      lowercase__			       =  in_proj_bias[: config.hidden_size]
      lowercase__			       =  in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase__			       =  in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowercase__			       =  in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase__			       =  in_proj_bias[-config.hidden_size :]


def _A       (   ):
   lowercase__			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase__			       =  Image.open(requests.get(lowercase__  , stream=lowercase__	).raw	)
   return im


@torch.no_grad()
def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__       ,							lowercase__			       =  get_dpt_config(lowercase__	)
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   lowercase__			       =  torch.load(lowercase__  , map_location="cpu"	)
   # remove certain keys
   remove_ignore_keys_(lowercase__	)
   # rename keys
   for key in state_dict.copy().keys():
      lowercase__			       =  state_dict.pop(lowercase__	)
      lowercase__			       =  val
   # read in qkv matrices
   read_in_q_k_v(lowercase__  , lowercase__	)

   # load HuggingFace model
   lowercase__			       =  DPTForSemanticSegmentation(lowercase__	) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase__	)
   model.load_state_dict(lowercase__	)
   model.eval()

   # Check outputs on an image
   lowercase__			       =  480 if "ade" in checkpoint_url else 384
   lowercase__			       =  DPTImageProcessor(size=lowercase__	)

   lowercase__			       =  prepare_img()
   lowercase__			       =  image_processor(lowercase__  , return_tensors="pt"	)

   # forward pass
   lowercase__			       =  model(**lowercase__	).logits if "ade" in checkpoint_url else model(**lowercase__	).predicted_depth

   if show_prediction:
      lowercase__			       =  (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1	)  , size=(image.size[1], image.size[0])  , mode="bicubic"  , align_corners=lowercase__  , )
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 255	).show()

   if pytorch_dump_folder_path is not None:
      Path(lowercase__	).mkdir(exist_ok=lowercase__	)
      print(f'''Saving model to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(lowercase__	)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
      image_processor.save_pretrained(lowercase__	)

   if push_to_hub:
      model.push_to_hub("ybelkada/dpt-hybrid-midas"	)
      image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
      type=str,
      help="""URL of the original DPT checkpoint you\'d like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=False,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  parser.add_argument(
      """--model_name""",
      default="""dpt-large""",
      type=str,
      help="""Name of the model, in case you\'re pushing to the hub.""",
  )
  parser.add_argument(
      """--show_prediction""",
      action="""store_true""",
  )

  _snake_case           =			parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )













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






