



'''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__					(  UpperCAmelCase_						,						**UpperCAmelCase_							):
						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 							lowercase__			(							lowercase     ):





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       ArgumentParser				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     parser.add_parser('run_beam'				,help='Run a Beam dataset processing pipeline'				)
							run_beam_parser.add_argument('dataset'				,type=lowerCamelCase__				,help='Name of the dataset to download'				)
							run_beam_parser.add_argument('--name'				,type=lowerCamelCase__				,default=lowerCamelCase__				,help='Dataset config name'				)
							run_beam_parser.add_argument(
							    '--cache_dir'				,type=lowerCamelCase__				,default=lowerCamelCase__				,help='Cache directory where the datasets are stored'				,)
							run_beam_parser.add_argument(
							    '--beam_pipeline_options'				,type=lowerCamelCase__				,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=lowerCamelCase__				,default=lowerCamelCase__				,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=lowerCamelCase__				)





	def __init__(				self      :       Union[str, Any]				,lowerCamelCase__      :       str				,lowerCamelCase__      :       str				,lowerCamelCase__      :       str				,lowerCamelCase__      :       str				,lowerCamelCase__      :       str				,lowerCamelCase__      :       bool				,lowerCamelCase__      :       bool				,lowerCamelCase__      :       bool				,lowerCamelCase__      :       bool				,**lowerCamelCase__      :       str				,):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     dataset
							_UpperCamelCase      :	Dict				       =     name
							_UpperCamelCase      :	int				       =     cache_dir
							_UpperCamelCase      :	List[str]				       =     beam_pipeline_options
							_UpperCamelCase      :	int				       =     data_dir
							_UpperCamelCase      :	Tuple				       =     all_configs
							_UpperCamelCase      :	Any				       =     save_infos
							_UpperCamelCase      :	Union[str, Any]				       =     ignore_verifications
							_UpperCamelCase      :	List[str]				       =     force_redownload
							_UpperCamelCase      :	Tuple				       =     config_kwargs





	def 			UpperCamelCase_						(				self      :       List[Any]				):







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

													_UpperCamelCase      :	str				       =     Path(lowerCamelCase__				).name + '.py'

													_UpperCamelCase      :	Any				       =     os.path.join(lowerCamelCase__				,lowerCamelCase__				)
													if os.path.isfile(lowerCamelCase__				):
																			_UpperCamelCase      :	List[Any]				       =     os.path.dirname(lowerCamelCase__				)
													elif os.path.isfile(lowerCamelCase__				):
																			_UpperCamelCase      :	str				       =     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
													_UpperCamelCase      :	Optional[Any]				       =     os.path.join(lowerCamelCase__				,config.DATASETDICT_INFOS_FILENAME				)
													copyfile(lowerCamelCase__				,lowerCamelCase__				)
													print(F'Dataset Infos file saved at {user_dataset_infos_path}'				)







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.


