




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__			(		A__			:   Tuple    ,     **A__			:   Optional[Any]      ):





       '''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__(   __lowerCamelCase):
      @staticmethod
      def      lowerCAmelCase__		(							UpperCamelCase_:					ArgumentParser  ):
             __lowerCamelCase  =		parser.add_parser("""run_beam"""       ,			help="""Run a Beam dataset processing pipeline"""  )
             run_beam_parser.add_argument("""dataset"""       ,			type=UpperCamelCase_       ,			help="""Name of the dataset to download"""  )
             run_beam_parser.add_argument("""--name"""       ,			type=UpperCamelCase_       ,			default=UpperCamelCase_       ,			help="""Dataset config name"""  )
             run_beam_parser.add_argument(
                 """--cache_dir"""       ,			type=UpperCamelCase_       ,			default=UpperCamelCase_       ,			help="""Cache directory where the datasets are stored"""       ,			)
             run_beam_parser.add_argument(
                 """--beam_pipeline_options"""       ,			type=UpperCamelCase_       ,			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=UpperCamelCase_       ,			default=UpperCamelCase_       ,			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=UpperCamelCase_  )
      def __init__(							self:					str       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					bool       ,			UpperCamelCase_:					bool       ,			UpperCamelCase_:					bool       ,			UpperCamelCase_:					bool       ,			**UpperCamelCase_:					List[str]       ,			):
             __lowerCamelCase  =		dataset
             __lowerCamelCase  =		name
             __lowerCamelCase  =		cache_dir
             __lowerCamelCase  =		beam_pipeline_options
             __lowerCamelCase  =		data_dir
             __lowerCamelCase  =		all_configs
             __lowerCamelCase  =		save_infos
             __lowerCamelCase  =		ignore_verifications
             __lowerCamelCase  =		force_redownload
             __lowerCamelCase  =		config_kwargs




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

                    __lowerCamelCase  =		Path(UpperCamelCase_  ).name + """.py"""

                    __lowerCamelCase  =		os.path.join(UpperCamelCase_       ,			UpperCamelCase_  )
                    if os.path.isfile(UpperCamelCase_  ):
                           __lowerCamelCase  =		os.path.dirname(UpperCamelCase_  )
                    elif os.path.isfile(UpperCamelCase_  ):
                           __lowerCamelCase  =		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  =		os.path.join(UpperCamelCase_       ,			config.DATASETDICT_INFOS_FILENAME  )
                    copyfile(UpperCamelCase_       ,			UpperCamelCase_  )
                    print(F'Dataset Infos file saved at {user_dataset_infos_path}'  )







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    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   bool      ):





       '''simple docstring'''



       if not inplace:
              __lowerCamelCase  =		ma + ma
       else:
              ma += ma

       return ma




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   List[int]      ):





       '''simple docstring'''



       __lowerCamelCase  =		-1 * len(A__      )
       __lowerCamelCase  =		list(range(len(tensor.shape[:zero_index]      )      )      )
       return tensor.permute(first_inds + [zero_index + i for i in inds]      )




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int    ,     A__			:   float = 1E-4      ):





       '''simple docstring'''



       __lowerCamelCase  =		mask.expand(*value.shape      )
       return torch.sum(mask * value    ,     dim=A__      ) / (eps + torch.sum(A__    ,     dim=A__      ))




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.types.Number = 2.3_125    ,     A__			:   torch.types.Number = 21.6_875    ,     A__			:   int = 64      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.linspace(A__    ,     A__    ,     no_bins - 1    ,     device=pts.device      )
       __lowerCamelCase  =		torch.sqrt(torch.sum((pts.unsqueeze(-2      ) - pts.unsqueeze(-3      )) ** 2    ,     dim=-1      )      )
       return torch.bucketize(A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   Callable[[list], Any]    ,     A__			:   List[dict]      ):





       '''simple docstring'''



       __lowerCamelCase  =		dicts[0]
       __lowerCamelCase  =		{}
       for k, v in first.items():
              __lowerCamelCase  =		[d[k] for d in dicts]
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_multimap(A__    ,     A__      )
              else:
                     __lowerCamelCase  =		fn(A__      )

       return new_dict




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int = 0    ,     A__			:   int = 0      ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       for i, s in enumerate(data.shape[:no_batch_dims]      ):
              __lowerCamelCase  =		torch.arange(A__      )
              __lowerCamelCase  =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape      ) - i - 1)))      )
              ranges.append(A__      )

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


UpperCAmelCase_   							=      TypeVar('T')




def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   Dict[Any, Union[dict, list, tuple, T]]    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       for k, v in dic.items():
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_map(A__    ,     A__    ,     A__      )
              else:
                     __lowerCamelCase  =		tree_map(A__    ,     A__    ,     A__      )

       return new_dict




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   T    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   dict    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   list    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   tuple    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




def    lowerCamelCase__			(		A__			:   Dict    ,     A__			:   List[str]    ,     A__			:   List[str]      ):





       '''simple docstring'''



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




UpperCAmelCase_   							=      partial(tree_map, leaf_type=torch.Tensor)

