






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__				)  ->      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 	lowerCamelCase__				(							UpperCAmelCase ):






      @staticmethod
      def      UpperCAmelCase_     (_snake_case					:    ArgumentParser		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     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					:    Optional[Any]		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    bool		,		_snake_case					:    bool		,		_snake_case					:    bool		,		_snake_case					:    bool		,		**_snake_case					:    Optional[Any]		,		)       ->   Dict:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    int		)       ->   List[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		)
       lowerCamelCase_		,      lowerCamelCase_							:    Dict							=     self._dataset, self._name
       lowerCamelCase_							:    Tuple							=     dataset_module_factory(_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     import_main_class(dataset_module.module_path		)
       lowerCamelCase_							:    List[DatasetBuilder]							=     []
       if self._beam_pipeline_options:
        lowerCamelCase_							:    int							=     beam.options.pipeline_options.PipelineOptions(
            flags=[f'--{opt.strip()}' for opt in self._beam_pipeline_options.split(','		) if opt]		)
       else:
        lowerCamelCase_							:    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:
        lowerCamelCase_							:    List[Any]							=     os.path.join(builder_cls.get_imported_module_dir()		,		config.DATASETDICT_INFOS_FILENAME		)

        lowerCamelCase_							:    List[str]							=     Path(_snake_case		).name + '.py'

        lowerCamelCase_							:    Optional[Any]							=     os.path.join(_snake_case		,		_snake_case		)
        if os.path.isfile(_snake_case		):
         lowerCamelCase_							:    Union[str, Any]							=     os.path.dirname(_snake_case		)
        elif os.path.isfile(_snake_case		):
         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_							:    Optional[int]							=     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}'		)








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   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 # 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:
  lowerCamelCase_							:    Tuple							=     ma + ma
 else:
  ma += ma

 return ma




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    Union[str, Any]							=     -1 * len(lowerCamelCase__				)
 lowerCamelCase_							:    Optional[Any]							=     list(range(len(tensor.shape[:zero_index]				)				)				)
 return tensor.permute(first_inds + [zero_index + i for i in inds]				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 return t.reshape(t.shape[:-no_dims] + (-1,)				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 1e-4				)  ->      torch.Tensor:
 lowerCamelCase_							:    Optional[int]							=     mask.expand(*value.shape				)
 return torch.sum(mask * value ,			dim=lowerCamelCase__				) / (eps + torch.sum(lowerCamelCase__ ,			dim=lowerCamelCase__				))




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ = 2.3125 ,			lowerCamelCase__ = 21.6875 ,			lowerCamelCase__ = 64				)  ->      torch.Tensor:
 lowerCamelCase_							:    int							=     torch.linspace(lowerCamelCase__ ,			lowerCamelCase__ ,			no_bins - 1 ,			device=pts.device				)
 lowerCamelCase_							:    Tuple							=     torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2 ,			dim=-1				)				)
 return torch.bucketize(lowerCamelCase__ ,			lowerCamelCase__				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 lowerCamelCase_							:    Optional[Any]							=     dicts[0]
 lowerCamelCase_							:    Optional[Any]							=     {}
 for k, v in first.items():
  lowerCamelCase_							:    Tuple							=     [d[k] for d in dicts]
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[int]							=     dict_multimap(lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    List[Any]							=     fn(lowerCamelCase__				)

 return new_dict




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    str							=     v_bins.view(((1,) * len(x.shape				)) + (len(lowerCamelCase__				),)				)
 lowerCamelCase_							:    List[Any]							=     x[..., None] - reshaped_bins
 lowerCamelCase_							:    List[Any]							=     torch.argmin(torch.abs(lowerCamelCase__				) ,			dim=-1				)
 return nn.functional.one_hot(lowerCamelCase__ ,			num_classes=len(lowerCamelCase__				)				).float()




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 0 ,			lowerCamelCase__ = 0				)  ->      torch.Tensor:
 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     []
 for i, s in enumerate(data.shape[:no_batch_dims]				):
  lowerCamelCase_							:    int							=     torch.arange(lowerCamelCase__				)
  lowerCamelCase_							:    Any							=     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
  ranges.append(lowerCamelCase__				)

 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     [slice(lowerCamelCase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
 lowerCamelCase_							:    Optional[int]							=     inds
 ranges.extend(lowerCamelCase__				)
 # 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(lowerCamelCase__				)]


UpperCamelCase											=      TypeVar('''T''')




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Dict[Any, Union[dict, list, tuple, Any]]:
 lowerCamelCase_							:    Dict[Any, Union[dict, list, tuple, Any]]							=     {}
 for k, v in dic.items():
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[Any]							=     dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    Dict							=     tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)

 return new_dict




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      list:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      tuple:
 ...




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return [tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree]
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return tuple(tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return fn(lowerCamelCase__				)
 else:
  print(type(lowerCamelCase__				)				)
  raise ValueError('Not supported'				)





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