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  _A  (			SCREAMING_SNAKE_CASE  ,**SCREAMING_SNAKE_CASE			):
  	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       _UpperCAmelCase						(							lowerCamelCase__	):
  						UpperCAmelCase__:  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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			**lowerCamelCase__							,			):
  						UpperCAmelCase__:  List[str]		       =   dataset
  						UpperCAmelCase__:  List[str]		       =   name
  						UpperCAmelCase__:  str		       =   cache_dir
  						UpperCAmelCase__:  Optional[Any]		       =   beam_pipeline_options
  						UpperCAmelCase__:  Union[str, Any]		       =   data_dir
  						UpperCAmelCase__:  int		       =   all_configs
  						UpperCAmelCase__:  List[str]		       =   save_infos
  						UpperCAmelCase__:  Optional[int]		       =   ignore_verifications
  						UpperCAmelCase__:  Union[str, Any]		       =   force_redownload
  						UpperCAmelCase__:  Any		       =   config_kwargs
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						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__:  List[str]		       =   self._dataset, self._name
  						UpperCAmelCase__:  List[Any]		       =   dataset_module_factory(lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   import_main_class(dataset_module.module_path	)
  						UpperCAmelCase__:  List[DatasetBuilder]		       =   []
  						if self._beam_pipeline_options:
  							UpperCAmelCase__:  Union[str, Any]		       =   beam.options.pipeline_options.PipelineOptions(
  							    flags=[F"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","	) if opt]	)
  						else:
  							UpperCAmelCase__:  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=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__:  Any		       =   os.path.join(builder_cls.get_imported_module_dir()							,			config.DATASETDICT_INFOS_FILENAME	)
  
  							UpperCAmelCase__:  Dict		       =   Path(lowerCamelCase__	).name + ".py"
  
  							UpperCAmelCase__:  Tuple		       =   os.path.join(lowerCamelCase__							,			lowerCamelCase__	)
  							if os.path.isfile(lowerCamelCase__	):
  								UpperCAmelCase__:  Optional[int]		       =   os.path.dirname(lowerCamelCase__	)
  							elif os.path.isfile(lowerCamelCase__	):
  								UpperCAmelCase__:  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
  							UpperCAmelCase__:  Optional[int]		       =   os.path.join(lowerCamelCase__							,			config.DATASETDICT_INFOS_FILENAME	)
  							copyfile(lowerCamelCase__							,			lowerCamelCase__	)
  							print(F"Dataset Infos file saved at {user_dataset_infos_path}"	)
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[Any]		       =   [
  	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  
  	for i in range(1  ,len_source_seq + 1			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"
  
  	for i in range(1  ,len_source_seq + 1			):
  		for j in range(1  ,len_destination_seq + 1			):
  			if source_seq[i - 1] == destination_seq[j - 1]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =0
  
  					with open("""min_cost.txt""", """w""") as file:
  										for op in sequence:
  															print("""""".join(string))
  
  															if op[0] == "C":
  																				file.write("""%-16s""" % """Copy %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost -= 1
  															elif op[0] == "R":
  																				_lowerCAmelCase     :			Tuple    =op[2]
  
  																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
  																				file.write("""\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 1
  															elif op[0] == "D":
  																				string.pop(i)
  
  																				file.write("""%-16s""" % """Delete %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  															else:
  																				string.insert(i, op[1])
  
  																				file.write("""%-16s""" % """Insert %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  
  															i += 1
  
  										print("""""".join(string))
  										print("""Cost: """, cost)
  
  										file.write("""\r\nMinimum cost: """ + str(cost))