




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				(				_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE   )     ->			str:
       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			,**_SCREAMING_SNAKE_CASE			,)




class 		UpperCamelCase__     (A__					):

    '''simple docstring'''



    @staticmethod
    def 	_lowercase					(   UpperCamelCase__					)					->				Union[str, Any]:
           lowerCamelCase       :				Tuple      			= 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					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					,  )					->				Tuple:
           lowerCamelCase       :				Dict      			= dataset
           lowerCamelCase       :				Optional[Any]      			= name
           lowerCamelCase       :				List[Any]      			= cache_dir
           lowerCamelCase       :				List[str]      			= beam_pipeline_options
           lowerCamelCase       :				Optional[int]      			= data_dir
           lowerCamelCase       :				Dict      			= all_configs
           lowerCamelCase       :				int      			= save_infos
           lowerCamelCase       :				Optional[Any]      			= ignore_verifications
           lowerCamelCase       :				Any      			= force_redownload
           lowerCamelCase       :				Tuple      			= config_kwargs







    def 	_lowercase					(   self					)					->				Optional[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       :				str      			= self._dataset, self._name
           lowerCamelCase       :				Optional[int]      			= dataset_module_factory(_A					)
           lowerCamelCase       :				List[str]      			= import_main_class(dataset_module.module_path					)
           lowerCamelCase       :				List[DatasetBuilder]      			= []
           if self._beam_pipeline_options:
                  lowerCamelCase       :				List[Any]      			= beam.options.pipeline_options.PipelineOptions(
                      flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(","					) if opt]					)
           else:
                  lowerCamelCase       :				Union[str, Any]      			= 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:
                  lowerCamelCase       :				str      			= os.path.join(builder_cls.get_imported_module_dir()					,  config.DATASETDICT_INFOS_FILENAME					)

                  lowerCamelCase       :				Optional[Any]      			= Path(_A					).name + ".py"

                  lowerCamelCase       :				int      			= os.path.join(_A					,  _A					)
                  if os.path.isfile(_A					):
                         lowerCamelCase       :				Optional[int]      			= os.path.dirname(_A					)
                  elif os.path.isfile(_A					):
                         lowerCamelCase       :				Dict      			= 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       :				Any      			= os.path.join(_A					,  config.DATASETDICT_INFOS_FILENAME					)
                  copyfile(_A					,  _A					)
                  print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






