


'''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		(lowercase__ :      Union[str, Any]       ,      **lowercase__ :      List[str]	) -> List[str]:






     """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  _lowercase   (  _UpperCAmelCase       ):
   @staticmethod
   def a     (	SCREAMING_SNAKE_CASE_					:			int			)   ->       List[Any]:
        __snake_case    			=	parser.add_parser('run_beam'					,      help='Run a Beam dataset processing pipeline'			)
        run_beam_parser.add_argument('dataset'					,      type=SCREAMING_SNAKE_CASE_					,      help='Name of the dataset to download'			)
        run_beam_parser.add_argument('--name'					,      type=SCREAMING_SNAKE_CASE_					,      default=SCREAMING_SNAKE_CASE_					,      help='Dataset config name'			)
        run_beam_parser.add_argument(
            '--cache_dir'					,      type=SCREAMING_SNAKE_CASE_					,      default=SCREAMING_SNAKE_CASE_					,      help='Cache directory where the datasets are stored'					,      )
        run_beam_parser.add_argument(
            '--beam_pipeline_options'					,      type=SCREAMING_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=SCREAMING_SNAKE_CASE_					,      default=SCREAMING_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=SCREAMING_SNAKE_CASE_			)
   def __init__(	self					:			str					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      **SCREAMING_SNAKE_CASE_					:			List[Any]					,      )   ->       Dict:
        __snake_case    			=	dataset
        __snake_case    			=	name
        __snake_case    			=	cache_dir
        __snake_case    			=	beam_pipeline_options
        __snake_case    			=	data_dir
        __snake_case    			=	all_configs
        __snake_case    			=	save_infos
        __snake_case    			=	ignore_verifications
        __snake_case    			=	force_redownload
        __snake_case    			=	config_kwargs





   def a     (	self					:			Any			)   ->       Union[str, Any]:
        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			)
        __snake_case			,					__snake_case    			=	self._dataset, self._name
        __snake_case    			=	dataset_module_factory(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	import_main_class(dataset_module.module_path			)
        __snake_case    			=	[]
        if self._beam_pipeline_options:
             __snake_case    			=	beam.options.pipeline_options.PipelineOptions(
                 flags=[f'--{opt.strip()}' for opt in self._beam_pipeline_options.split(','			) if opt]			)
        else:
             __snake_case    			=	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=SCREAMING_SNAKE_CASE_					,      cache_dir=self._cache_dir					,      base_path=dataset_module.builder_kwargs.get('base_path'			)					,      )			)
        else:
             builders.append(
                 builder_cls(
                     config_name=SCREAMING_SNAKE_CASE_					,      data_dir=self._data_dir					,      beam_options=SCREAMING_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=SCREAMING_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:
             __snake_case    			=	os.path.join(builder_cls.get_imported_module_dir()					,      config.DATASETDICT_INFOS_FILENAME			)

             __snake_case    			=	Path(SCREAMING_SNAKE_CASE_			).name + '.py'

             __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             if os.path.isfile(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	os.path.dirname(SCREAMING_SNAKE_CASE_			)
             elif os.path.isfile(SCREAMING_SNAKE_CASE_			):
                  __snake_case    			=	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
             __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      config.DATASETDICT_INFOS_FILENAME			)
             copyfile(SCREAMING_SNAKE_CASE_					,      SCREAMING_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      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      bool	) -> torch.Tensor:






     """simple docstring"""
     # The first operation in a checkpoint can't be in-place, but it's
     # nice to have in-place addition during inference. Thus...
     if not inplace:
          __snake_case    			=	ma + ma
     else:
          ma += ma

     return ma





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      List[int]	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	-1 * len(lowercase__	)
     __snake_case    			=	list(range(len(tensor.shape[:zero_index]	)	)	)
     return tensor.permute(first_inds + [zero_index + i for i in inds]	)





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      int	) -> torch.Tensor:






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





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int       ,      lowercase__ :      float = 1e-4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	mask.expand(*value.shape	)
     return torch.sum(mask * value       ,      dim=lowercase__	) / (eps + torch.sum(lowercase__       ,      dim=lowercase__	))





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.types.Number = 2.31_25       ,      lowercase__ :      torch.types.Number = 21.68_75       ,      lowercase__ :      int = 6_4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	torch.linspace(lowercase__       ,      lowercase__       ,      no_bins - 1       ,      device=pts.device	)
     __snake_case    			=	torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2       ,      dim=-1	)	)
     return torch.bucketize(lowercase__       ,      lowercase__	)





def      _a		(lowercase__ :      Callable[[list], Any]       ,      lowercase__ :      List[dict]	) -> dict:






     """simple docstring"""
     __snake_case    			=	dicts[0]
     __snake_case    			=	{}
     for k, v in first.items():
          __snake_case    			=	[d[k] for d in dicts]
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_multimap(lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	fn(lowercase__	)

     return new_dict





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	v_bins.view(((1,) * len(x.shape	)) + (len(lowercase__	),)	)
     __snake_case    			=	x[..., None] - reshaped_bins
     __snake_case    			=	torch.argmin(torch.abs(lowercase__	)       ,      dim=-1	)
     return nn.functional.one_hot(lowercase__       ,      num_classes=len(lowercase__	)	).float()





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int = 0       ,      lowercase__ :      int = 0	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	[]
     for i, s in enumerate(data.shape[:no_batch_dims]	):
          __snake_case    			=	torch.arange(lowercase__	)
          __snake_case    			=	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
          ranges.append(lowercase__	)

     __snake_case    			=	[slice(lowercase__	) for _ in range(len(data.shape	) - no_batch_dims	)]
     __snake_case    			=	inds
     ranges.extend(lowercase__	)
     # 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(lowercase__	)]


_a      :      str       				=      TypeVar("T")





def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      Dict[Any, Union[dict, list, tuple, T]]       ,      lowercase__ :      Type[T]	) -> Dict[Any, Union[dict, list, tuple, Any]]:






     """simple docstring"""
     __snake_case    			=	{}
     for k, v in dic.items():
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_map(lowercase__       ,      lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	tree_map(lowercase__       ,      lowercase__       ,      lowercase__	)

     return new_dict





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      T       ,      lowercase__ :      Type[T]	) -> Any:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      dict       ,      lowercase__ :      Type[T]	) -> dict:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      list       ,      lowercase__ :      Type[T]	) -> list:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      tuple       ,      lowercase__ :      Type[T]	) -> tuple:






     """simple docstring"""
     ...





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      Dict	) -> Union[str, Any]:






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


_a      :      Union[str, Any]       				=      partial(tree_map, leaf_type=torch.Tensor)




