"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase       :						str     =   logging.get_logger(__name__)

_UpperCamelCase       :						Tuple     =   {
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class 					UpperCAmelCase_     (   _a):
						lowerCamelCase__		:   Tuple			  =  '''yoso'''


						def __init__(       self		,      a=5_0_2_6_5		,      a=7_6_8		,      a=1_2		,      a=1_2		,      a=3_0_7_2		,      a="gelu"		,      a=0.1		,      a=0.1		,      a=4_0_9_6		,      a=1		,      a=0.02		,      a=1e-12		,      a="absolute"		,      a=True		,      a=9		,      a=6_4		,      a=None		,      a=True		,      a=True		,      a=1		,      a=0		,      a=2		,      **a		,      )	-> Any:
													super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,      bos_token_id=SCREAMING_SNAKE_CASE__		,      eos_token_id=SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__							)

													lowercase__      :		Optional[Any]          =     vocab_size
													lowercase__      :		List[Any]          =     max_position_embeddings
													lowercase__      :		Dict          =     hidden_size
													lowercase__      :		Any          =     num_hidden_layers
													lowercase__      :		int          =     num_attention_heads
													lowercase__      :		Any          =     intermediate_size
													lowercase__      :		Any          =     hidden_act
													lowercase__      :		Any          =     hidden_dropout_prob
													lowercase__      :		Tuple          =     attention_probs_dropout_prob
													lowercase__      :		Optional[int]          =     initializer_range
													lowercase__      :		Tuple          =     type_vocab_size
													lowercase__      :		Tuple          =     layer_norm_eps
													lowercase__      :		List[Any]          =     position_embedding_type
													lowercase__      :		Dict          =     use_expectation
													lowercase__      :		Dict          =     hash_code_len
													lowercase__      :		Dict          =     num_hash
													lowercase__      :		str          =     conv_window
													lowercase__      :		List[Any]          =     use_fast_hash
													lowercase__      :		str          =     lsh_backward


"""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_					(				_lowerCAmelCase  :			Tuple       ,			**_lowerCAmelCase  :			Any					):





       '''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       ,			**_lowerCAmelCase       ,			)






class 					UpperCAmelCase_     (   _a):


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> Optional[int]:
             lowercase__      :		Optional[int]          =     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		,      )	-> Any:
             lowercase__      :		List[Any]          =     dataset
             lowercase__      :		List[Any]          =     name
             lowercase__      :		List[Any]          =     cache_dir
             lowercase__      :		Any          =     beam_pipeline_options
             lowercase__      :		List[str]          =     data_dir
             lowercase__      :		int          =     all_configs
             lowercase__      :		Optional[Any]          =     save_infos
             lowercase__      :		List[Any]          =     ignore_verifications
             lowercase__      :		int          =     force_redownload
             lowercase__      :		List[Any]          =     config_kwargs


      def 		_UpperCAmelCase	(       self							)	-> int:
             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							)
             lowercase__   ,				lowercase__      :		Optional[int]          =     self._dataset, self._name
             lowercase__      :		List[str]          =     dataset_module_factory(a							)
             lowercase__      :		Dict          =     import_main_class(dataset_module.module_path							)
             lowercase__      :		List[DatasetBuilder]          =     []
             if self._beam_pipeline_options:
                    lowercase__      :		Any          =     beam.options.pipeline_options.PipelineOptions(
                        flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','							) if opt]							)
             else:
                    lowercase__      :		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=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:
                    lowercase__      :		Dict          =     os.path.join(builder_cls.get_imported_module_dir()		,      config.DATASETDICT_INFOS_FILENAME							)

                    lowercase__      :		Union[str, Any]          =     Path(a							).name + '.py'

                    lowercase__      :		List[Any]          =     os.path.join(a		,      a							)
                    if os.path.isfile(a							):
                           lowercase__      :		Any          =     os.path.dirname(a							)
                    elif os.path.isfile(a							):
                           lowercase__      :		Optional[int]          =     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
                    lowercase__      :		List[Any]          =     os.path.join(a		,      config.DATASETDICT_INFOS_FILENAME							)
                    copyfile(a		,      a							)
                    print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""							)

