

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   __SCREAMING_SNAKE_CASE (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      snake_case_       (		__A ):







			'''simple docstring'''

			@staticmethod
			def     snake_case__(					_UpperCamelCase			:    ArgumentParser							)				->Tuple:
									snake_case_       =     parser.add_parser('''run_beam'''							,       help='''Run a Beam dataset processing pipeline'''							)
									run_beam_parser.add_argument('''dataset'''							,       type=_UpperCamelCase							,       help='''Name of the dataset to download'''							)
									run_beam_parser.add_argument('''--name'''							,       type=_UpperCamelCase							,       default=_UpperCamelCase							,       help='''Dataset config name'''							)
									run_beam_parser.add_argument(
									    '''--cache_dir'''							,       type=_UpperCamelCase							,       default=_UpperCamelCase							,       help='''Cache directory where the datasets are stored'''							,       )
									run_beam_parser.add_argument(
									    '''--beam_pipeline_options'''							,       type=_UpperCamelCase							,       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=_UpperCamelCase							,       default=_UpperCamelCase							,       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=_UpperCamelCase							)







			def __init__(					self			:    Dict							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    bool							,       _UpperCamelCase			:    bool							,       **_UpperCamelCase			:    List[Any]							,       )				->Tuple:
									snake_case_       =     dataset
									snake_case_       =     name
									snake_case_       =     cache_dir
									snake_case_       =     beam_pipeline_options
									snake_case_       =     data_dir
									snake_case_       =     all_configs
									snake_case_       =     save_infos
									snake_case_       =     ignore_verifications
									snake_case_       =     force_redownload
									snake_case_       =     config_kwargs







			def     snake_case__(					self			:    Union[str, Any]							)				->Tuple:
									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							)
									snake_case_,   snake_case_       =     self._dataset, self._name
									snake_case_       =     dataset_module_factory(_UpperCamelCase							)
									snake_case_       =     import_main_class(dataset_module.module_path							)
									snake_case_       =     []
									if self._beam_pipeline_options:
															snake_case_       =     beam.options.pipeline_options.PipelineOptions(
															    flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''							) if opt]							)
									else:
															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=_UpperCamelCase							,       cache_dir=self._cache_dir							,       base_path=dataset_module.builder_kwargs.get('''base_path'''							)							,       )							)
									else:
															builders.append(
															    builder_cls(
															        config_name=_UpperCamelCase							,       data_dir=self._data_dir							,       beam_options=_UpperCamelCase							,       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=_UpperCamelCase							,       )
															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:
															snake_case_       =     os.path.join(builder_cls.get_imported_module_dir()							,       config.DATASETDICT_INFOS_FILENAME							)

															snake_case_       =     Path(_UpperCamelCase							).name + '''.py'''

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


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''							)
									if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''							)

									super().__init__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor