






import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


A_		:		Dict		     =			getLogger(__name__)

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = 8     ,   UpperCAmelCase__ = 1_0_2_4     ,   UpperCAmelCase__="val"     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=False     ,   UpperCAmelCase__="summarization"     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=1     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__=""     ,   **UpperCAmelCase__     ,   )	->    Dict:
  UpperCamelCase_: List[str]   				=    str(UpperCAmelCase__  )
  assert local_rank is not None
  torch.distributed.init_process_group(backend='nccl'     ,   rank=UpperCAmelCase__  )

  UpperCamelCase_: List[Any]   				=    Path(UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    save_dir.joinpath(F'''rank_{local_rank}_output.json'''  )
  torch.cuda.set_device(UpperCAmelCase__  )
  UpperCamelCase_: Union[str, Any]   				=    AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__  ).cuda()
  if fpaa:
    UpperCamelCase_: Union[str, Any]   				=    model.half()
  # determine if we need to increase num_beams
  use_task_specific_params(UpperCAmelCase__     ,   UpperCAmelCase__  )  # update config with task specific params
  UpperCamelCase_: List[str]   				=    generate_kwargs.pop('num_beams'     ,   model.config.num_beams  )  # AttributeError risk?
  if num_return_sequences > num_beams:
    UpperCamelCase_: Dict   				=    num_return_sequences

  UpperCamelCase_: Optional[Any]   				=    AutoTokenizer.from_pretrained(UpperCAmelCase__  )
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''  )  # if this is wrong, check config.model_type.

  if max_source_length is None:
    UpperCamelCase_: List[str]   				=    tokenizer.model_max_length
  if prefix is None:
    UpperCamelCase_: Any   				=    prefix or getattr(model.config     ,   'prefix'     ,   ''  ) or ''
  UpperCamelCase_: Dict   				=    SeqaSeqDataset(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   max_target_length=1_0_2_4     ,   type_path=UpperCAmelCase__     ,   n_obs=UpperCAmelCase__     ,   prefix=UpperCAmelCase__     ,   **UpperCAmelCase__     ,   )
  # I set shuffle=True for a more accurate progress bar.
  # If all the longest samples are first, the prog bar estimate is too high at the beginning.
  UpperCamelCase_: Dict   				=    ds.make_sortish_sampler(UpperCAmelCase__     ,   distributed=UpperCAmelCase__     ,   add_extra_examples=UpperCAmelCase__     ,   shuffle=UpperCAmelCase__  )
  UpperCamelCase_: Optional[Any]   				=    DataLoader(UpperCAmelCase__     ,   sampler=UpperCAmelCase__     ,   batch_size=UpperCAmelCase__     ,   collate_fn=ds.collate_fn  )
  UpperCamelCase_: Dict   				=    []
  for batch in tqdm(UpperCAmelCase__  ):
    UpperCamelCase_: Tuple   				=    model.generate(
        input_ids=batch['input_ids'].to(model.device  )     ,   attention_mask=batch['attention_mask'].to(model.device  )     ,   num_return_sequences=UpperCAmelCase__     ,   num_beams=UpperCAmelCase__     ,   **UpperCAmelCase__     ,   )
    UpperCamelCase_: Any   				=    tokenizer.batch_decode(UpperCAmelCase__     ,   skip_special_tokens=UpperCAmelCase__     ,   clean_up_tokenization_spaces=UpperCAmelCase__  )
    UpperCamelCase_: List[str]   				=    batch['ids']
    if num_return_sequences > 1:
      UpperCamelCase_: Optional[Any]   				=    chunks(UpperCAmelCase__     ,   UpperCAmelCase__  )  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(UpperCAmelCase__  ):
      results.append({'pred': pred, 'id': ids[i].item()}  )
  save_json(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return results, sampler.num_replicas

def   snake_case ()	->    List[str]:
  UpperCamelCase_: Union[str, Any]   				=    argparse.ArgumentParser(
      epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'  )
  parser.add_argument('--data_dir'     ,   type=UpperCAmelCase__     ,   help='like cnn_dm/test.source'  )
  parser.add_argument(
      '--model_name'     ,   type=UpperCAmelCase__     ,   help='like facebook/bart-large-cnn,t5-base, etc.'     ,   default='sshleifer/distilbart-xsum-12-3'     ,   )
  parser.add_argument('--save_dir'     ,   type=UpperCAmelCase__     ,   help='where to save'     ,   default='tmp_gen'  )
  parser.add_argument('--max_source_length'     ,   type=UpperCAmelCase__     ,   default=UpperCAmelCase__  )
  parser.add_argument(
      '--type_path'     ,   type=UpperCAmelCase__     ,   default='test'     ,   help='which subset to evaluate typically train/val/test'  )
  parser.add_argument('--task'     ,   type=UpperCAmelCase__     ,   default='summarization'     ,   help='used for task_specific_params + metrics'  )
  parser.add_argument('--bs'     ,   type=UpperCAmelCase__     ,   default=8     ,   required=UpperCAmelCase__     ,   help='batch size'  )
  parser.add_argument(
      '--local_rank'     ,   type=UpperCAmelCase__     ,   default=-1     ,   required=UpperCAmelCase__     ,   help='should be passed by distributed.launch'  )

  parser.add_argument(
      '--n_obs'     ,   type=UpperCAmelCase__     ,   default=UpperCAmelCase__     ,   required=UpperCAmelCase__     ,   help='How many observations. Defaults to all.'  )
  parser.add_argument(
      '--num_return_sequences'     ,   type=UpperCAmelCase__     ,   default=1     ,   required=UpperCAmelCase__     ,   help='How many sequences to return'  )
  parser.add_argument(
      '--sync_timeout'     ,   type=UpperCAmelCase__     ,   default=6_0_0     ,   required=UpperCAmelCase__     ,   help='How long should master process wait for other processes to finish.'     ,   )
  parser.add_argument('--src_lang'     ,   type=UpperCAmelCase__     ,   default=UpperCAmelCase__     ,   required=UpperCAmelCase__  )
  parser.add_argument('--tgt_lang'     ,   type=UpperCAmelCase__     ,   default=UpperCAmelCase__     ,   required=UpperCAmelCase__  )
  parser.add_argument(
      '--prefix'     ,   type=UpperCAmelCase__     ,   required=UpperCAmelCase__     ,   default=UpperCAmelCase__     ,   help='will be added to the begininng of src examples'  )
  parser.add_argument('--fp16'     ,   action='store_true'  )
  parser.add_argument('--debug'     ,   action='store_true'  )
  UpperCamelCase_: List[str]   				=    time.time()
  UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    parser.parse_known_args()
  UpperCamelCase_: Optional[Any]   				=    parse_numeric_n_bool_cl_kwargs(UpperCAmelCase__  )
  if generate_kwargs and args.local_rank <= 0:
    print(F'''parsed the following generate kwargs: {generate_kwargs}'''  )
  UpperCamelCase_: int   				=    Path(args.save_dir + '_tmp'  )
  Path(UpperCAmelCase__  ).mkdir(exist_ok=UpperCAmelCase__  )  # this handles locking.
  UpperCamelCase_: Dict   				=    list(json_save_dir.glob('rank_*.json'  )  )
  if intermediate_files:
    raise ValueError(F'''Found files at {json_save_dir} please move or remove them.'''  )
    # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
  UpperCamelCase_: Any   				=    {}
  if args.src_lang is not None:
    UpperCamelCase_: Optional[Any]   				=    args.src_lang
  if args.tgt_lang is not None:
    UpperCamelCase_: List[Any]   				=    args.tgt_lang

  Path(args.save_dir  ).mkdir(exist_ok=UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    eval_data_dir(
      args.data_dir     ,   UpperCAmelCase__     ,   args.model_name     ,   type_path=args.type_path     ,   bs=args.bs     ,   fpaa=args.fpaa     ,   task=args.task     ,   local_rank=args.local_rank     ,   n_obs=args.n_obs     ,   max_source_length=args.max_source_length     ,   num_return_sequences=args.num_return_sequences     ,   prefix=args.prefix     ,   dataset_kwargs=UpperCAmelCase__     ,   **UpperCAmelCase__     ,   )

  if args.local_rank <= 0:
    UpperCamelCase_: List[str]   				=    Path(args.save_dir  )
    save_dir.mkdir(exist_ok=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    gather_results_from_each_node(UpperCAmelCase__     ,   UpperCAmelCase__     ,   args.sync_timeout  )
    UpperCamelCase_: Tuple   				=    combine_partial_results(UpperCAmelCase__  )
    if args.num_return_sequences > 1:
      UpperCamelCase_: str   				=    save_dir.joinpath('pseudolabel_results.json'  )
      print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''  )
      save_json(UpperCAmelCase__     ,   UpperCAmelCase__  )
      return
    UpperCamelCase_: str   				=    Path(args.data_dir  ).joinpath(args.type_path + '.target'  )
    with open(UpperCAmelCase__  ) as f:
      UpperCamelCase_: Optional[Any]   				=    [x.rstrip() for x in f.readlines()][: len(UpperCAmelCase__  )]

    # Calculate metrics, save metrics,  and save _generations.txt
    UpperCamelCase_: Optional[int]   				=    'translation' in args.task
    UpperCamelCase_: List[Any]   				=    calculate_bleu if calc_bleu else calculate_rouge
    UpperCamelCase_: int   				=    'bleu' if calc_bleu else 'rouge'
    UpperCamelCase_: Dict   				=    score_fn(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: List[str]   				=    len(UpperCAmelCase__  )
    UpperCamelCase_: int   				=    time.time() - start_time
    UpperCamelCase_: Any   				=    round(runtime / metrics['n_obs']     ,   4  )
    UpperCamelCase_: Union[str, Any]   				=    num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    UpperCamelCase_: Dict   				=    save_dir.joinpath(F'''{args.type_path}_{metric_name}.json'''  )
    save_json(UpperCAmelCase__     ,   UpperCAmelCase__     ,   indent=UpperCAmelCase__  )
    print(UpperCAmelCase__  )
    write_txt_file(UpperCAmelCase__     ,   save_dir.joinpath(F'''{args.type_path}_generations.txt'''  )  )
    if args.debug:
      write_txt_file(UpperCAmelCase__     ,   save_dir.joinpath(F'''{args.type_path}.target'''  )  )
    else:
      shutil.rmtree(UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__  )	->    List:
  UpperCamelCase_: int   				=    []
  for partial_result in partial_results:
    records.extend(UpperCAmelCase__  )
  UpperCamelCase_: Optional[Any]   				=    sorted(UpperCAmelCase__     ,   key=lambda UpperCAmelCase__  : x["id"]  )
  UpperCamelCase_: Dict   				=    [x['pred'] for x in records]
  return preds

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Dict[str, List]]:
  # WAIT FOR lots of .json files
  UpperCamelCase_: Any   				=    time.time()
  logger.info('waiting for all nodes to finish'  )
  UpperCamelCase_: Tuple   				=    None
  while (time.time() - start_wait) < timeout:
    UpperCamelCase_: Any   				=    list(save_dir.glob('rank_*.json'  )  )
    if len(UpperCAmelCase__  ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      UpperCamelCase_: str   				=    lmap(UpperCAmelCase__     ,   UpperCAmelCase__  )
      return json_data
    except JSONDecodeError:
      continue
  else:
    raise TimeoutError('Rank 0 gave up on waiting for other processes'  )
  # Unreachable


if __name__ == "__main__":
   # Usage for MT:
   run_generate()







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