code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A_														=      {
      "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
      "tokenization_esm": ["EsmTokenizer"],
  }
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "EsmForMaskedLM",
  		    "EsmForSequenceClassification",
  		    "EsmForTokenClassification",
  		    "EsmModel",
  		    "EsmPreTrainedModel",
  		]
  		A_														=      ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "TFEsmForMaskedLM",
  		    "TFEsmForSequenceClassification",
  		    "TFEsmForTokenClassification",
  		    "TFEsmModel",
  		    "TFEsmPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  		from .tokenization_esm import EsmTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_esm import (
  				    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    EsmForMaskedLM,
  				    EsmForSequenceClassification,
  				    EsmForTokenClassification,
  				    EsmModel,
  				    EsmPreTrainedModel,
  				)
  				from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_esm import (
  				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    TFEsmForMaskedLM,
  				    TFEsmForSequenceClassification,
  				    TFEsmForTokenClassification,
  				    TFEsmModel,
  				    TFEsmPreTrainedModel,
  				)
  
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure)
  
  
  
style_context: 
  
  
  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 							UpperCAmelCase				(     UpperCAmelCase				,**UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					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 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@staticmethod
  			def _lowercase (	lowerCAmelCase_     :     ArgumentParser					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       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     :     Any							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							**lowerCAmelCase_     :     Any							,							)					->					Tuple:
  
  
  
  
  								"""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 _lowercase (	self     :     List[str]					)					->					int:
  
  
  
  
  								"""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(lowerCAmelCase_					)
  								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=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:
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(builder_cls.get_imported_module_dir()							,							config.DATASETDICT_INFOS_FILENAME					)
  
  													SCREAMING_SNAKE_CASE_	      =       Path(lowerCAmelCase_					).name + '''.py'''
  
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							lowerCAmelCase_					)
  													if os.path.isfile(lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       os.path.dirname(lowerCAmelCase_					)
  													elif os.path.isfile(lowerCAmelCase_					):
  																		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(lowerCAmelCase_							,							config.DATASETDICT_INFOS_FILENAME					)
  													copyfile(lowerCAmelCase_							,							lowerCAmelCase_					)
  													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)
  
  
  