code: 
  
  
  
  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 			__lowercase     (				a__							,     **a__     )       ->				List[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							,     **a__							,     )
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      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   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      dataset
  													__SCREAMING_SNAKE_CASE								=      name
  													__SCREAMING_SNAKE_CASE								=      cache_dir
  													__SCREAMING_SNAKE_CASE								=      beam_pipeline_options
  													__SCREAMING_SNAKE_CASE								=      data_dir
  													__SCREAMING_SNAKE_CASE								=      all_configs
  													__SCREAMING_SNAKE_CASE								=      save_infos
  													__SCREAMING_SNAKE_CASE								=      ignore_verifications
  													__SCREAMING_SNAKE_CASE								=      force_redownload
  													__SCREAMING_SNAKE_CASE								=      config_kwargs
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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	)
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self._dataset, self._name
  													__SCREAMING_SNAKE_CASE								=      dataset_module_factory(_A	)
  													__SCREAMING_SNAKE_CASE								=      import_main_class(dataset_module.module_path	)
  													__SCREAMING_SNAKE_CASE								=      []
  													if self._beam_pipeline_options:
  																				__SCREAMING_SNAKE_CASE								=      beam.options.pipeline_options.PipelineOptions(
  																				    flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','	) if opt]	)
  													else:
  																				__SCREAMING_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=_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:
  																				__SCREAMING_SNAKE_CASE								=      os.path.join(builder_cls.get_imported_module_dir()   ,							config.DATASETDICT_INFOS_FILENAME	)
  
  																				__SCREAMING_SNAKE_CASE								=      Path(_A	).name + '.py'
  
  																				__SCREAMING_SNAKE_CASE								=      os.path.join(_A   ,							_A	)
  																				if os.path.isfile(_A	):
  																											__SCREAMING_SNAKE_CASE								=      os.path.dirname(_A	)
  																				elif os.path.isfile(_A	):
  																											__SCREAMING_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
  																				__SCREAMING_SNAKE_CASE								=      os.path.join(_A   ,							config.DATASETDICT_INFOS_FILENAME	)
  																				copyfile(_A   ,							_A	)
  																				print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  import unittest
  
  from cer import CER
  
  
  lowerCAmelCase__	:  Dict 		=CER()
  
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['White House']
  													__SCREAMING_SNAKE_CASE								=      ['white house']
  													# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['were wolf']
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['weae     wolf']
  													# S = 1, D = 1, I = 0, N = 8, CER = 0.25
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.2_5	) < 1e-6	)
  
  													# consecutive whitespaces case 1
  													__SCREAMING_SNAKE_CASE								=      ['were wolf']
  													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
  													# S = 0, D = 0, I = 0, N = 9, CER = 0
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)
  
  													# consecutive whitespaces case 2
  													__SCREAMING_SNAKE_CASE								=      ['were   wolf']
  													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
  													# S = 0, D = 0, I = 0, N = 9, CER = 0
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['weaewolf']
  													# S = 1, D = 0, I = 0, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['wereawolf']
  													# S = 0, D = 1, I = 0, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['wereolf']
  													# S = 0, D = 0, I = 1, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertEqual(_A   ,							0.0	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertEqual(_A   ,							0.0	)
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father', 'doge']
  													__SCREAMING_SNAKE_CASE								=      ['werxwolf', 'I       am your father', 'doge']
  													# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['My hovercraft', 'is full of eels']
  													__SCREAMING_SNAKE_CASE								=      ['My hovercraft is full', ' of eels']
  													# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  													# whitespace at the front of " of eels" will be strip during preporcessing
  													# so need to insert 2 whitespaces
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A   ,							concatenate_texts=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      [' 能吞虾玻璃而 不霜身体啦']
  													# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃', '而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      ['我    能 吞 下 玻 璃', '而不伤身体']
  													# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertFalse(_A   ,							0.0	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['']
  													__SCREAMING_SNAKE_CASE								=      ['Hypothesis']
  													with self.assertRaises(_A	):
  																				cer.compute(predictions=_A   ,							references=_A	)
  
  
  if __name__ == "__main__":
  			unittest.main()
  
  
  
  
  
  
  