


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  lowerCAmelCase__				(    a__:	int    ,						**a__:	Union[str, Any] )						->	Union[str, 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 	__a   (       snake_case__    ):
		@staticmethod
		def 			UpperCAmelCase__       (    _SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    parser.add_parser('run_beam'    ,					help='Run a Beam dataset processing pipeline'		)
									run_beam_parser.add_argument('dataset'    ,					type=_SCREAMING_SNAKE_CASE    ,					help='Name of the dataset to download'		)
									run_beam_parser.add_argument('--name'    ,					type=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					help='Dataset config name'		)
									run_beam_parser.add_argument(
									    '--cache_dir'    ,					type=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					help='Cache directory where the datasets are stored'    ,					)
									run_beam_parser.add_argument(
									    '--beam_pipeline_options'    ,					type=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE    ,					default=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE		)
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> int:




									"""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		)				-> Dict:




									"""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					=    self._dataset, self._name
									_UpperCAmelCase					=    dataset_module_factory(_SCREAMING_SNAKE_CASE		)
									_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=_SCREAMING_SNAKE_CASE    ,					cache_dir=self._cache_dir    ,					base_path=dataset_module.builder_kwargs.get('base_path'		)    ,					)		)
									else:
																builders.append(
																    builder_cls(
																        config_name=_SCREAMING_SNAKE_CASE    ,					data_dir=self._data_dir    ,					beam_options=_SCREAMING_SNAKE_CASE    ,					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=_SCREAMING_SNAKE_CASE    ,					)
																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(_SCREAMING_SNAKE_CASE		).name + '.py'

																_UpperCAmelCase					=    os.path.join(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																if os.path.isfile(_SCREAMING_SNAKE_CASE		):
																							_UpperCAmelCase					=    os.path.dirname(_SCREAMING_SNAKE_CASE		)
																elif os.path.isfile(_SCREAMING_SNAKE_CASE		):
																							_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(_SCREAMING_SNAKE_CASE    ,					config.DATASETDICT_INFOS_FILENAME		)
																copyfile(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''		)




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

lowerCAmelCase__						:List[str]         =							logging.get_logger(__name__)

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									if token_ids_a is None:
																return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""simple docstring"""





									if not self.can_save_slow_tokenizer:
																raise ValueError(
																    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																    'tokenizer.'		)

									if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		):
																copyfile(self.vocab_file    ,					_SCREAMING_SNAKE_CASE		)

									return (out_vocab_file,)
