





"""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 						UpperCAmelCase__		(					_UpperCAmelCase			, **_UpperCAmelCase ):






  """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			, **_UpperCAmelCase			, )





class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''

      @staticmethod
      def  lowerCamelCase_      (					snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  parser.add_parser('run_beam' ,				help='Run a Beam dataset processing pipeline'  )
        run_beam_parser.add_argument('dataset' ,				type=snake_case_ ,				help='Name of the dataset to download'  )
        run_beam_parser.add_argument('--name' ,				type=snake_case_ ,				default=snake_case_ ,				help='Dataset config name'  )
        run_beam_parser.add_argument(
            '--cache_dir' ,				type=snake_case_ ,				default=snake_case_ ,				help='Cache directory where the datasets are stored' ,				)
        run_beam_parser.add_argument(
            '--beam_pipeline_options' ,				type=snake_case_ ,				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=snake_case_ ,				default=snake_case_ ,				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=snake_case_  )

      def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				**snake_case_ ,				):

        """simple docstring"""

        A_  :	List[Any]    						=  dataset
        A_  :	Union[str, Any]    						=  name
        A_  :	List[str]    						=  cache_dir
        A_  :	Tuple    						=  beam_pipeline_options
        A_  :	str    						=  data_dir
        A_  :	Union[str, Any]    						=  all_configs
        A_  :	int    						=  save_infos
        A_  :	Tuple    						=  ignore_verifications
        A_  :	Optional[int]    						=  force_redownload
        A_  :	int    						=  config_kwargs

      def  lowerCamelCase_      (					self  ):

        """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  )
        A_  :	Union[str, Any]    						=  self._dataset, self._name
        A_  :	Union[str, Any]    						=  dataset_module_factory(snake_case_  )
        A_  :	Optional[int]    						=  import_main_class(dataset_module.module_path  )
        A_  :	List[DatasetBuilder]    						=  []
        if self._beam_pipeline_options:
          A_  :	int    						=  beam.options.pipeline_options.PipelineOptions(
              flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','  ) if opt]  )
        else:
          A_  :	Optional[int]    						=  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=snake_case_ ,				cache_dir=self._cache_dir ,				base_path=dataset_module.builder_kwargs.get('base_path'  ) ,				)  )
        else:
          builders.append(
              builder_cls(
                  config_name=snake_case_ ,				data_dir=self._data_dir ,				beam_options=snake_case_ ,				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=snake_case_ ,				)
          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:
          A_  :	List[Any]    						=  os.path.join(builder_cls.get_imported_module_dir() ,				config.DATASETDICT_INFOS_FILENAME  )

          A_  :	int    						=  Path(snake_case_  ).name + '.py'

          A_  :	List[str]    						=  os.path.join(snake_case_ ,				snake_case_  )
          if os.path.isfile(snake_case_  ):
            A_  :	Optional[int]    						=  os.path.dirname(snake_case_  )
          elif os.path.isfile(snake_case_  ):
            A_  :	Union[str, 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
          A_  :	List[str]    						=  os.path.join(snake_case_ ,				config.DATASETDICT_INFOS_FILENAME  )
          copyfile(snake_case_ ,				snake_case_  )
          print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""  )











"""simple docstring"""




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if not inplace:
    A_  :	Optional[int]    						=  ma + ma
  else:
    ma += ma

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  -1 * len(_UpperCAmelCase )
  A_  :	List[str]    						=  list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  return t.reshape(t.shape[:-no_dims] + (-1,) )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





  A_  :	List[str]    						=  torch.linspace(_UpperCAmelCase			, _UpperCAmelCase			, no_bins - 1			, device=pts.device )
  A_  :	Optional[Any]    						=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2			, dim=-1 ) )
  return torch.bucketize(_UpperCAmelCase			, _UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  dicts[0]
  A_  :	int    						=  {}
  for k, v in first.items():
    A_  :	Dict    						=  [d[k] for d in dicts]
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	List[Any]    						=  dict_multimap(_UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Union[str, Any]    						=  fn(_UpperCAmelCase )

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  v_bins.view(((1,) * len(x.shape )) + (len(_UpperCAmelCase ),) )
  A_  :	Any    						=  x[..., None] - reshaped_bins
  A_  :	List[str]    						=  torch.argmin(torch.abs(_UpperCAmelCase )			, dim=-1 )
  return nn.functional.one_hot(_UpperCAmelCase			, num_classes=len(_UpperCAmelCase ) ).float()





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





  A_  :	List[Union[slice, torch.Tensor]]    						=  []
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    A_  :	List[Any]    						=  torch.arange(_UpperCAmelCase )
    A_  :	Dict    						=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(_UpperCAmelCase )

  A_  :	List[Union[slice, torch.Tensor]]    						=  [slice(_UpperCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
  A_  :	Optional[Any]    						=  inds
  ranges.extend(_UpperCAmelCase )
  # Matt note: Editing this to get around the behaviour of using a list as an array index changing
  # in recent Numpy versions
  return data[tuple(_UpperCAmelCase )]


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return [tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree]
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return tuple(tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return fn(_UpperCAmelCase )
  else:
    print(type(_UpperCAmelCase ) )
    raise ValueError('Not supported' )





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)