
"""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		(							snake_case :int							,   **snake_case :int						) ->      Tuple:
 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							,   **lowerCAmelCase__							,   )





class __lowerCAmelCase						( SCREAMING_SNAKE_CASE__     ):





     @staticmethod
     def 		UpperCAmelCase   (      __UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			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      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			dataset
      __UpperCamelCase							    =			name
      __UpperCamelCase							    =			cache_dir
      __UpperCamelCase							    =			beam_pipeline_options
      __UpperCamelCase							    =			data_dir
      __UpperCamelCase							    =			all_configs
      __UpperCamelCase							    =			save_infos
      __UpperCamelCase							    =			ignore_verifications
      __UpperCamelCase							    =			force_redownload
      __UpperCamelCase							    =			config_kwargs







     def 		UpperCAmelCase   (      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 )
      __UpperCamelCase    ,      __UpperCamelCase							    =			self._dataset, self._name
      __UpperCamelCase							    =			dataset_module_factory(A__ )
      __UpperCamelCase							    =			import_main_class(dataset_module.module_path )
      __UpperCamelCase							    =			[]
      if self._beam_pipeline_options:
       __UpperCamelCase							    =			beam.options.pipeline_options.PipelineOptions(
           flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(',' ) if opt] )
      else:
       __UpperCamelCase							    =			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:
       __UpperCamelCase							    =			os.path.join(builder_cls.get_imported_module_dir()      ,	config.DATASETDICT_INFOS_FILENAME )

       __UpperCamelCase							    =			Path(A__ ).name + '.py'

       __UpperCamelCase							    =			os.path.join(A__      ,	A__ )
       if os.path.isfile(A__ ):
        __UpperCamelCase							    =			os.path.dirname(A__ )
       elif os.path.isfile(A__ ):
        __UpperCamelCase							    =			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							    =			os.path.join(A__      ,	config.DATASETDICT_INFOS_FILENAME )
       copyfile(A__      ,	A__ )
       print(F'Dataset Infos file saved at {user_dataset_infos_path}' )





import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




