


"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case        =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")



@dataclass
class 	_a   :
    a_     :					Optional[str]   							=     field(
        default='cifar10'		,		metadata={'help': 'Name of a dataset from the datasets package'}			)
    a_     :					Optional[str]   							=     field(
        default=_a		,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
    a_     :					Optional[str]   							=     field(
        default=_a		,		metadata={'help': 'The column name of the images in the files.'}			)
    a_     :					Optional[str]   							=     field(default=_a		,		metadata={'help': 'A folder containing the training data.'}			)
    a_     :					Optional[str]   							=     field(default=_a		,		metadata={'help': 'A folder containing the validation data.'}			)
    a_     :					Optional[float]   							=     field(
        default=0.15		,		metadata={'help': 'Percent to split off of train for validation.'}			)
    a_     :					Optional[int]   							=     field(
        default=_a		,		metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of training examples to this '
                'value if set.'
            )
        }		,		)
    a_     :					Optional[int]   							=     field(
        default=_a		,		metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                'value if set.'
            )
        }		,		)
    def   _UpperCamelCase							(			self				:       List[str]					):
          lowerCamelCase__							     =		{}
          if self.train_dir is not None:
                lowerCamelCase__							     =		self.train_dir
          if self.validation_dir is not None:
                lowerCamelCase__							     =		self.validation_dir
          lowerCamelCase__							     =		data_files if data_files else None





@dataclass
class 	_a   :
    a_     :					str   							=     field(
        default=_a		,		metadata={
            'help': (
                'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
            )
        }		,		)
    a_     :					Optional[str]   							=     field(
        default=_a		,		metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
    a_     :					Optional[str]   							=     field(
        default=_a		,		metadata={
            'help': (
                'Override some existing default config settings when a model is trained from scratch. Example: '
                'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
            )
        }		,		)
    a_     :					Optional[str]   							=     field(
        default=_a		,		metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
    a_     :					str   							=     field(
        default='main'		,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,		)
    a_     :					str   							=     field(default=_a		,		metadata={'help': 'Name or path of preprocessor config.'}			)
    a_     :					bool   							=     field(
        default=_a		,		metadata={
            'help': (
                'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                'with private models).'
            )
        }		,		)
    a_     :					float   							=     field(
        default=0.75		,		metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
    a_     :					bool   							=     field(
        default=_a		,		metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class 	_a   (   _a			):
    a_     :					float   							=     field(
        default=1e-3		,		metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)





def 	snake_case    (					_a:  Optional[int]				)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__							     =		torch.stack([example['pixel_values'] for example in examples]				)
      return {"pixel_values": pixel_values}



def 	snake_case    (					)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__							     =		HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
      if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            lowerCamelCase__     ,	lowerCamelCase__     ,	lowerCamelCase__							     =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            lowerCamelCase__     ,	lowerCamelCase__     ,	lowerCamelCase__							     =		parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('run_mae'		, UpperCamelCase__		, UpperCamelCase__				)

      # Setup logging
      logging.basicConfig(
          format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		, datefmt='%m/%d/%Y %H:%M:%S'		, handlers=[logging.StreamHandler(sys.stdout				)]		, )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      lowerCamelCase__							     =		training_args.get_process_log_level()
      logger.setLevel(UpperCamelCase__				)
      transformers.utils.logging.set_verbosity(UpperCamelCase__				)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
          + F'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
      logger.info(F'Training/evaluation parameters {training_args}'				)

      # Detecting last checkpoint.
      lowerCamelCase__							     =		None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCamelCase__							     =		get_last_checkpoint(training_args.output_dir				)
            if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
                  raise ValueError(
                      F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                      'Use --overwrite_output_dir to overcome.'				)
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                      'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

                   # Initialize our dataset.
      lowerCamelCase__							     =		load_dataset(
          data_args.dataset_name		, data_args.dataset_config_name		, data_files=data_args.data_files		, cache_dir=model_args.cache_dir		, use_auth_token=True if model_args.use_auth_token else None		, )

      # If we don't have a validation split, split off a percentage of train as validation.
      lowerCamelCase__							     =		None if 'validation' in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split		, UpperCamelCase__				) and data_args.train_val_split > 0.0:
            lowerCamelCase__							     =		ds['train'].train_test_split(data_args.train_val_split				)
            lowerCamelCase__							     =		split['train']
            lowerCamelCase__							     =		split['test']

      # Load pretrained model and image processor
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCamelCase__							     =		{
          'cache_dir': model_args.cache_dir,
          'revision': model_args.model_revision,
          'use_auth_token': True if model_args.use_auth_token else None,
      }
      if model_args.config_name:
            lowerCamelCase__							     =		ViTMAEConfig.from_pretrained(model_args.config_name		, **UpperCamelCase__				)
      elif model_args.model_name_or_path:
            lowerCamelCase__							     =		ViTMAEConfig.from_pretrained(model_args.model_name_or_path		, **UpperCamelCase__				)
      else:
            lowerCamelCase__							     =		ViTMAEConfig()
            logger.warning('You are instantiating a new config instance from scratch.'				)
            if model_args.config_overrides is not None:
                  logger.info(F'Overriding config: {model_args.config_overrides}'				)
                  config.update_from_string(model_args.config_overrides				)
                  logger.info(F'New config: {config}'				)

                   # adapt config
      config.update(
          {
              'mask_ratio': model_args.mask_ratio,
              'norm_pix_loss': model_args.norm_pix_loss,
          }				)

      # create image processor
      if model_args.image_processor_name:
            lowerCamelCase__							     =		ViTImageProcessor.from_pretrained(model_args.image_processor_name		, **UpperCamelCase__				)
      elif model_args.model_name_or_path:
            lowerCamelCase__							     =		ViTImageProcessor.from_pretrained(model_args.model_name_or_path		, **UpperCamelCase__				)
      else:
            lowerCamelCase__							     =		ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            lowerCamelCase__							     =		ViTMAEForPreTraining.from_pretrained(
                model_args.model_name_or_path		, from_tf=bool('.ckpt' in model_args.model_name_or_path				)		, config=UpperCamelCase__		, cache_dir=model_args.cache_dir		, revision=model_args.model_revision		, use_auth_token=True if model_args.use_auth_token else None		, )
      else:
            logger.info('Training new model from scratch'				)
            lowerCamelCase__							     =		ViTMAEForPreTraining(UpperCamelCase__				)

      if training_args.do_train:
            lowerCamelCase__							     =		ds['train'].column_names
      else:
            lowerCamelCase__							     =		ds['validation'].column_names

      if data_args.image_column_name is not None:
            lowerCamelCase__							     =		data_args.image_column_name
      elif "image" in column_names:
            lowerCamelCase__							     =		'image'
      elif "img" in column_names:
            lowerCamelCase__							     =		'img'
      else:
            lowerCamelCase__							     =		column_names[0]

      # transformations as done in original MAE paper
      # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
      if "shortest_edge" in image_processor.size:
            lowerCamelCase__							     =		image_processor.size['shortest_edge']
      else:
            lowerCamelCase__							     =		(image_processor.size['height'], image_processor.size['width'])
      lowerCamelCase__							     =		Compose(
          [
              Lambda(lambda _a				: img.convert('RGB'				) if img.mode != "RGB" else img				),
              RandomResizedCrop(UpperCamelCase__		, scale=(0.2, 1.0)		, interpolation=InterpolationMode.BICUBIC				),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean		, std=image_processor.image_std				),
          ]				)

      def preprocess_images(_a:  Optional[int]				):
            lowerCamelCase__							     =		[transforms(UpperCamelCase__				) for image in examples[image_column_name]]
            return examples

      if training_args.do_train:
            if "train" not in ds:
                  raise ValueError('--do_train requires a train dataset'				)
            if data_args.max_train_samples is not None:
                  lowerCamelCase__							     =		ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
            # Set the training transforms
            ds["train"].set_transform(UpperCamelCase__				)

      if training_args.do_eval:
            if "validation" not in ds:
                  raise ValueError('--do_eval requires a validation dataset'				)
            if data_args.max_eval_samples is not None:
                  lowerCamelCase__							     =		(
                      ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(UpperCamelCase__				)

      # Compute absolute learning rate
      lowerCamelCase__							     =		(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            lowerCamelCase__							     =		training_args.base_learning_rate * total_train_batch_size / 256

      # Initialize our trainer
      lowerCamelCase__							     =		Trainer(
          model=UpperCamelCase__		, args=UpperCamelCase__		, train_dataset=ds['train'] if training_args.do_train else None		, eval_dataset=ds['validation'] if training_args.do_eval else None		, tokenizer=UpperCamelCase__		, data_collator=UpperCamelCase__		, )

      # Training
      if training_args.do_train:
            lowerCamelCase__							     =		None
            if training_args.resume_from_checkpoint is not None:
                  lowerCamelCase__							     =		training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCamelCase__							     =		last_checkpoint
            lowerCamelCase__							     =		trainer.train(resume_from_checkpoint=UpperCamelCase__				)
            trainer.save_model()
            trainer.log_metrics('train'		, train_result.metrics				)
            trainer.save_metrics('train'		, train_result.metrics				)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            lowerCamelCase__							     =		trainer.evaluate()
            trainer.log_metrics('eval'		, UpperCamelCase__				)
            trainer.save_metrics('eval'		, UpperCamelCase__				)

      # Write model card and (optionally) push to hub
      lowerCamelCase__							     =		{
          'tasks': 'masked-auto-encoding',
          'dataset': data_args.dataset_name,
          'tags': ['masked-auto-encoding'],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**UpperCamelCase__				)
      else:
            trainer.create_model_card(**UpperCamelCase__				)



def 	snake_case    (					_a:  List[Any]				)->		List[str]:
      '''simple docstring'''


      main()


if __name__ == "__main__":
   main()







"""simple docstring"""

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def lowerCAmelCase_   (					UpperCamelCase__						:			int			):  # picklable for multiprocessing






 """simple docstring"""







 return x.sum()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]			):  # picklable for multiprocessing






 """simple docstring"""







 return i + 1





@dataclass
class  lowerCamelCase__    :
      a			:  int
      a			:  str






class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''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(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_				)

       __lowercase						=						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_				)

       __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(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 SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''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(A_   ,							A_   ,							A_				)				)   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       class  lowerCamelCase__    :
             a			:  int           =		"""bar"""






       __lowercase						=						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 lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 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(UpperCamelCase__			)}
  __lowercase						=						map_nested(lambda UpperCamelCase__			: x + 10					, UpperCamelCase__					, num_proc=UpperCamelCase__					, parallel_min_length=16			)
  if expected_num_proc == 1:
   assert mock_single_map_nested.called
   assert not mock_multiprocessing_pool.called
  else:
   assert not mock_single_map_nested.called
   assert mock_multiprocessing_pool.called
   assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class  lowerCamelCase__    (		_a     ):
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''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(A_				).numpy()

       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       import torch

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

       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


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

       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

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






@pytest.mark.parametrize("""input_data"""					, [{}]			)
def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						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 lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).flatten()
 assert output == expected_output


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						A(x=1					, y="""foobar"""			)
 __lowercase						=						{"""x""": 1, """y""": """foobar"""}
 assert asdict(UpperCamelCase__			) == 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(UpperCamelCase__			) == expected_output

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


def lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 return text.split()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict			):






 """simple docstring"""







 yield (time.time(), content)
 time.sleep(2			)
 yield (time.time(), content)


def lowerCAmelCase_   (					):






 """simple docstring"""







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

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

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