


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__       (							A__,	**A__							):
			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,	**A__,	)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@staticmethod
							def  UpperCamelCase__			(	lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= 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       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= dataset
										SCREAMING_SNAKE_CASE_						:			int       						= name
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= cache_dir
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= beam_pipeline_options
										SCREAMING_SNAKE_CASE_						:			Any       						= data_dir
										SCREAMING_SNAKE_CASE_						:			Tuple       						= all_configs
										SCREAMING_SNAKE_CASE_						:			Tuple       						= save_infos
										SCREAMING_SNAKE_CASE_						:			str       						= ignore_verifications
										SCREAMING_SNAKE_CASE_						:			Any       						= force_redownload
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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    )
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self._dataset, self._name
										SCREAMING_SNAKE_CASE_						:			Tuple       						= dataset_module_factory(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= import_main_class(dataset_module.module_path    )
										SCREAMING_SNAKE_CASE_						:			List[DatasetBuilder]       						= []
										if self._beam_pipeline_options:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= beam.options.pipeline_options.PipelineOptions(
													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','    ) if opt]    )
										else:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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_						:			List[Any]       						= os.path.join(builder_cls.get_imported_module_dir()       ,		config.DATASETDICT_INFOS_FILENAME    )

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= Path(lowerCAmelCase__    ).name + '.py'

													SCREAMING_SNAKE_CASE_						:			int       						= os.path.join(lowerCAmelCase__       ,		lowerCAmelCase__    )
													if os.path.isfile(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= os.path.dirname(lowerCAmelCase__    )
													elif os.path.isfile(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 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_						:			Dict       						= os.path.join(lowerCAmelCase__       ,		config.DATASETDICT_INFOS_FILENAME    )
													copyfile(lowerCAmelCase__       ,		lowerCAmelCase__    )
													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''    )













"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


_UpperCamelCase				:    Optional[int]     =						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''speech-encoder-decoder'''
    __magic_name__         =     True





    def __init__(							self	:					Optional[Any]				,		**A	:					List[Any]			):


      '''simple docstring'''




      super().__init__(**A			)
      if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
            F''' `decoder` sub-configurations are passed, but only {kwargs}'''			)

      a     :			Optional[int]				=   kwargs.pop('encoder'			)
      a     :			Any				=   encoder_config.pop('model_type'			)
      a     :			Optional[Any]				=   kwargs.pop('decoder'			)
      a     :			str				=   decoder_config.pop('model_type'			)

      a     :			Tuple				=   AutoConfig.for_model(A				,		**A			)
      a     :			Dict				=   AutoConfig.for_model(A				,		**A			)
      a     :			Optional[Any]				=   True





    @classmethod
    def 				lowerCamelCase__      (							cls	:					List[Any]				,		A	:					PretrainedConfig				,		A	:					PretrainedConfig				,		**A	:					str			):


      '''simple docstring'''




      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
      a     :			Any				=   True
      a     :			List[str]				=   True

      return cls(encoder=encoder_config.to_dict()				,		decoder=decoder_config.to_dict()				,		**A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[Any]				=   copy.deepcopy(self.__dict__			)
      a     :			int				=   self.encoder.to_dict()
      a     :			Optional[int]				=   self.decoder.to_dict()
      a     :			Any				=   self.__class__.model_type
      return output




