



'''simple docstring'''





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode

def  a_  (							_lowerCAmelCase ,**_lowerCAmelCase )   ->   Optional[int]:

  '''simple docstring'''





  return RunBeamCommand(
      args.dataset ,args.name ,args.cache_dir ,args.beam_pipeline_options ,args.data_dir ,args.all_configs ,args.save_info or args.save_infos ,args.ignore_verifications ,args.force_redownload ,**_A ,)






class       lowerCamelCase_					(       _UpperCAmelCase  ):
  """simple docstring"""




  @staticmethod
  def 		_lowercase  (				_a   :			Dict     )      ->      Tuple:
    __lowerCamelCase : Tuple					 =				parser.add_parser('run_beam'    ,			help='Run a Beam dataset processing pipeline'     )
    run_beam_parser.add_argument('dataset'    ,			type=_a    ,			help='Name of the dataset to download'     )
    run_beam_parser.add_argument('--name'    ,			type=_a    ,			default=_a    ,			help='Dataset config name'     )
    run_beam_parser.add_argument(
        '--cache_dir'    ,			type=_a    ,			default=_a    ,			help='Cache directory where the datasets are stored'    ,			)
    run_beam_parser.add_argument(
        '--beam_pipeline_options'    ,			type=_a    ,			default=''    ,			help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'    ,			)
    run_beam_parser.add_argument(
        '--data_dir'    ,			type=_a    ,			default=_a    ,			help='Can be used to specify a manual directory to get the files from'    ,			)
    run_beam_parser.add_argument('--all_configs'    ,			action='store_true'    ,			help='Test all dataset configurations'     )
    run_beam_parser.add_argument('--save_info'    ,			action='store_true'    ,			help='Save the dataset infos file'     )
    run_beam_parser.add_argument(
        '--ignore_verifications'    ,			action='store_true'    ,			help='Run the test without checksums and splits checks'     )
    run_beam_parser.add_argument('--force_redownload'    ,			action='store_true'    ,			help='Force dataset redownload'     )
    # aliases
    run_beam_parser.add_argument('--save_infos'    ,			action='store_true'    ,			help='alias for save_info'     )
    run_beam_parser.set_defaults(func=_a     )




  def __init__(				self   :			Tuple    ,			_a   :			List[str]    ,			_a   :			Any    ,			_a   :			List[str]    ,			_a   :			Any    ,			_a   :			Union[str, Any]    ,			_a   :			str    ,			_a   :			Any    ,			_a   :			List[Any]    ,			_a   :			Any    ,			**_a   :			Union[str, Any]    ,			)      ->      str:
    __lowerCamelCase : Union[str, Any]					 =				dataset
    __lowerCamelCase : Optional[int]					 =				name
    __lowerCamelCase : List[str]					 =				cache_dir
    __lowerCamelCase : List[str]					 =				beam_pipeline_options
    __lowerCamelCase : Dict					 =				data_dir
    __lowerCamelCase : List[str]					 =				all_configs
    __lowerCamelCase : Union[str, Any]					 =				save_infos
    __lowerCamelCase : Dict					 =				ignore_verifications
    __lowerCamelCase : Dict					 =				force_redownload
    __lowerCamelCase : Any					 =				config_kwargs




  def 		_lowercase  (				self   :			int     )      ->      Dict:
    import apache_beam as beam

    if self._name is not None and self._all_configs:
      print('Both parameters `name` and `all_configs` can\'t be used at once.'     )
      exit(1     )
    __lowerCamelCase ,__lowerCamelCase : str					 =				self._dataset, self._name
    __lowerCamelCase : str					 =				dataset_module_factory(_a     )
    __lowerCamelCase : Any					 =				import_main_class(dataset_module.module_path     )
    __lowerCamelCase : Tuple					 =				[]
    if self._beam_pipeline_options:
      __lowerCamelCase : Optional[int]					 =				beam.options.pipeline_options.PipelineOptions(
          flags=[f'--{opt.strip()}' for opt in self._beam_pipeline_options.split(','     ) if opt]     )
    else:
      __lowerCamelCase : Union[str, Any]					 =				None
    if self._all_configs and len(builder_cls.BUILDER_CONFIGS     ) > 0:
      for builder_config in builder_cls.BUILDER_CONFIGS:
        builders.append(
            builder_cls(
                config_name=builder_config.name    ,			data_dir=self._data_dir    ,			hash=dataset_module.hash    ,			beam_options=_a    ,			cache_dir=self._cache_dir    ,			base_path=dataset_module.builder_kwargs.get('base_path'     )    ,			)     )
    else:
      builders.append(
          builder_cls(
              config_name=_a    ,			data_dir=self._data_dir    ,			beam_options=_a    ,			cache_dir=self._cache_dir    ,			base_path=dataset_module.builder_kwargs.get('base_path'     )    ,			**self._config_kwargs    ,			)     )

    for builder in builders:
      builder.download_and_prepare(
          download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
          if not self._force_redownload
          else DownloadMode.FORCE_REDOWNLOAD    ,			download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH     )    ,			verification_mode=VerificationMode.NO_CHECKS
          if self._ignore_verifications
          else VerificationMode.ALL_CHECKS    ,			try_from_hf_gcs=_a    ,			)
      if self._save_infos:
        builder._save_infos()

    print('Apache beam run successful.'     )

    # If save_infos=True, the dataset infos file is created next to the loaded module file.
    # Let's move it to the original directory of the dataset script, to allow the user to
    # upload them on S3 at the same time afterwards.
    if self._save_infos:
      __lowerCamelCase : Union[str, Any]					 =				os.path.join(builder_cls.get_imported_module_dir()    ,			config.DATASETDICT_INFOS_FILENAME     )

      __lowerCamelCase : List[str]					 =				Path(_a     ).name + '.py'

      __lowerCamelCase : str					 =				os.path.join(_a    ,			_a     )
      if os.path.isfile(_a     ):
        __lowerCamelCase : Union[str, Any]					 =				os.path.dirname(_a     )
      elif os.path.isfile(_a     ):
        __lowerCamelCase : Any					 =				path
      else:  # in case of a remote dataset
        print(f'Dataset Infos file saved at {dataset_infos_path}'     )
        exit(1     )

      # Move datasetinfo back to the user
      __lowerCamelCase : Dict					 =				os.path.join(_a    ,			config.DATASETDICT_INFOS_FILENAME     )
      copyfile(_a    ,			_a     )
      print(f'Dataset Infos file saved at {user_dataset_infos_path}'     )











'''simple docstring'''





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  @abstractmethod
  def __lt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __gt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __eq__(				self   :			T    ,			_a   :			object     )      ->      bool:
    pass




_UpperCamelCase							       =		TypeVar('T', bound=Comparable)






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			List[str]     )      ->      None:
    __lowerCamelCase : list[T]					 =				[]
    __lowerCamelCase : int					 =				0




  def __repr__(				self   :			int     )      ->      str:
    return str(self.h     )




  def 		_lowercase  (				self   :			str    ,			_a   :			int     )      ->      int | None:
    if child_idx > 0:
      return (child_idx - 1) // 2
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Optional[Any]					 =				2 * parent_idx + 1
    if left_child_index < self.heap_size:
      return left_child_index
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Any					 =				2 * parent_idx + 2
    if right_child_index < self.heap_size:
      return right_child_index
    return None




  def 		_lowercase  (				self   :			int    ,			_a   :			int     )      ->      None:
    if index < self.heap_size:
      __lowerCamelCase : int					 =				index
      __lowerCamelCase : Dict					 =				self.left_child_idx(_a     )
      __lowerCamelCase : str					 =				self.right_child_idx(_a     )
      # check which child is larger than its parent
      if left_child is not None and self.h[left_child] > self.h[violation]:
        __lowerCamelCase : Optional[int]					 =				left_child
      if right_child is not None and self.h[right_child] > self.h[violation]:
        __lowerCamelCase : Union[str, Any]					 =				right_child
      # if violation indeed exists
      if violation != index:
        # swap to fix the violation
        __lowerCamelCase ,__lowerCamelCase : int					 =				self.h[index], self.h[violation]
        # fix the subsequent violation recursively if any
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int    ,			_a   :			Iterable[T]     )      ->      None:
    __lowerCamelCase : Dict					 =				list(_a     )
    __lowerCamelCase : Any					 =				len(self.h     )
    if self.heap_size > 1:
      # max_heapify from right to left but exclude leaves (last level)
      for i in range(self.heap_size // 2 - 1    ,			-1    ,			-1     ):
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int     )      ->      T:
    if self.heap_size >= 2:
      __lowerCamelCase : Any					 =				self.h[0]
      __lowerCamelCase : Dict					 =				self.h.pop(-1     )
      self.heap_size -= 1
      self.max_heapify(0     )
      return me
    elif self.heap_size == 1:
      self.heap_size -= 1
      return self.h.pop(-1     )
    else:
      raise Exception('Empty heap'     )




  def 		_lowercase  (				self   :			Optional[Any]    ,			_a   :			T     )      ->      None:
    self.h.append(_a     )
    __lowerCamelCase : List[Any]					 =				(self.heap_size - 1) // 2
    self.heap_size += 1
    while idx >= 0:
      self.max_heapify(_a     )
      __lowerCamelCase : Union[str, Any]					 =				(idx - 1) // 2




  def 		_lowercase  (				self   :			Tuple     )      ->      None:
    __lowerCamelCase : Optional[Any]					 =				self.heap_size
    for j in range(size - 1    ,			0    ,			-1     ):
      __lowerCamelCase ,__lowerCamelCase : List[str]					 =				self.h[j], self.h[0]
      self.heap_size -= 1
      self.max_heapify(0     )
    __lowerCamelCase : Tuple					 =				size


if __name__ == "__main__":
   import doctest

   # run doc test
   doctest.testmod()

   # demo
   for unsorted in [
       [0],
       [2],
       [3, 5],
       [5, 3],
       [5, 5],
       [0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 3, 5],
       [0, 2, 2, 3, 5],
       [2, 5, 3, 0, 2, 3, 0, 3],
       [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
       [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
       [-45, -2, -5],
   ]:
      print(f'''unsorted array: {unsorted}''')

      _UpperCamelCase							       =		Heap()
      heap.build_max_heap(unsorted)
      print(f'''after build heap: {heap}''')

      print(f'''max value: {heap.extract_max()}''')
      print(f'''after max value removed: {heap}''')

      heap.insert(100)
      print(f'''after new value 100 inserted: {heap}''')

      heap.heap_sort()
      print(f'''heap-sorted array: {heap}\n''')






