

'''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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   int       ,   **SCREAMING_SNAKE_CASE      :   Tuple						):
  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       ,   **lowerCAmelCase__       ,   )



class        lowercase_	(		snake_case__ ):

  '''simple docstring'''





  @staticmethod
  def 						snake_case_			(     a_     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	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   , a_   , a_   , a_   , a_   , a_   , a_   , a_   , a_   , a_   , **a_   , )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	dataset
    UpperCAmelCase													=	name
    UpperCAmelCase													=	cache_dir
    UpperCAmelCase													=	beam_pipeline_options
    UpperCAmelCase													=	data_dir
    UpperCAmelCase													=	all_configs
    UpperCAmelCase													=	save_infos
    UpperCAmelCase													=	ignore_verifications
    UpperCAmelCase													=	force_redownload
    UpperCAmelCase													=	config_kwargs





  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    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     )
    UpperCAmelCase													=	self._dataset, self._name
    UpperCAmelCase													=	dataset_module_factory(a_     )
    UpperCAmelCase													=	import_main_class(dataset_module.module_path     )
    UpperCAmelCase													=	[]
    if self._beam_pipeline_options:
      UpperCAmelCase													=	beam.options.pipeline_options.PipelineOptions(
          flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','     ) if opt]     )
    else:
      UpperCAmelCase													=	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:
      UpperCAmelCase													=	os.path.join(builder_cls.get_imported_module_dir()   , config.DATASETDICT_INFOS_FILENAME     )

      UpperCAmelCase													=	Path(a_     ).name + '.py'

      UpperCAmelCase													=	os.path.join(a_   , a_     )
      if os.path.isfile(a_     ):
        UpperCAmelCase													=	os.path.dirname(a_     )
      elif os.path.isfile(a_     ):
        UpperCAmelCase													=	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
      UpperCAmelCase													=	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 collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  if is_torch_available():
    import torch

    if isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor						):
      return True

  return isinstance(SCREAMING_SNAKE_CASE       ,   np.ndarray						)



class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	fields(self     )

    # Safety and consistency checks
    if not len(a_     ):
      raise ValueError(F'''{self.__class__.__name__} has no fields.'''     )

    UpperCAmelCase													=	getattr(self   , class_fields[0].name     )
    UpperCAmelCase													=	all(getattr(self   , field.name     ) is None for field in class_fields[1:]     )

    if other_fields_are_none and isinstance(a_   , a_     ):
      for key, value in first_field.items():
        UpperCAmelCase													=	value
    else:
      for field in class_fields:
        UpperCAmelCase													=	getattr(self   , field.name     )
        if v is not None:
          UpperCAmelCase													=	v
  def __delitem__(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Tuple:


    """simple docstring"""
    raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


    """simple docstring"""
    raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )
  def __getitem__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	dict(self.items()     )
      return inner_dict[k]
    else:
      return self.to_tuple()[k]
  def __setattr__(     self   , a_   , a_     )							->				Dict:


    """simple docstring"""
    if name in self.keys() and value is not None:
      # Don't call self.__setitem__ to avoid recursion errors
      super().__setitem__(a_   , a_     )
    super().__setattr__(a_   , a_     )
  def __setitem__(     self   , a_   , a_     )							->				str:


    """simple docstring"""
    # Will raise a KeyException if needed
    super().__setitem__(a_   , a_     )
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(a_   , a_     )





  def 						snake_case_			(     self     )							->				Tuple[Any]:


    """simple docstring"""
    return tuple(self[k] for k in self.keys()     )


