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_ (			UpperCamelCase__    :		int,						**UpperCamelCase__    :		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,						**a__,						)







class      __lowercase  (       __A    ):




				'''simple docstring'''


				@staticmethod
				def 			A_				(						_a  :						List[str]							):
								UpperCamelCase__       						=							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  :						Optional[Any]	,				_a  :						Tuple	,				_a  :						List[str]	,				_a  :						str	,				_a  :						Optional[Any]	,				_a  :						Tuple	,				_a  :						List[str]	,				_a  :						List[Any]	,				_a  :						Dict	,				_a  :						Union[str, Any]	,				**_a  :						Optional[int]	,				):
								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 			A_				(						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							)
								UpperCamelCase__						,      UpperCamelCase__       						=							self._dataset, self._name
								UpperCamelCase__       						=							dataset_module_factory(lowerCamelCase__							)
								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=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:
												UpperCamelCase__       						=							os.path.join(builder_cls.get_imported_module_dir()	,				config.DATASETDICT_INFOS_FILENAME							)

												UpperCamelCase__       						=							Path(lowerCamelCase__							).name + '''.py'''

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









'''simple docstring'''
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A							:      Union[str, Any] 	=       logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		eos_token_id=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		max_new_tokens=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						text_encoding_offset
           __lowercase							=						text_pad_token
           __lowercase							=						eos_token_id
           __lowercase							=						semantic_infer_token
           __lowercase							=						semantic_vocab_size
           __lowercase							=						max_input_semantic_length
           __lowercase							=						semantic_rate_hz






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						coarse_semantic_pad_token
           __lowercase							=						coarse_rate_hz
           __lowercase							=						n_coarse_codebooks
           __lowercase							=						coarse_infer_token
           __lowercase							=						max_coarse_input_length
           __lowercase							=						max_coarse_history
           __lowercase							=						sliding_window_len






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """simple docstring"""



           if semantic_config is None:
               __lowercase							=						{}
               logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

           if coarse_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""     )

           if fine_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""     )

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """simple docstring"""



           return cls(
               semantic_config=semantic_config.to_dict()					,		coarse_acoustics_config=coarse_acoustics_config.to_dict()					,		fine_acoustics_config=fine_acoustics_config.to_dict()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

           __lowercase							=						self.semantic_config.to_dict()
           __lowercase							=						self.coarse_acoustics_config.to_dict()
           __lowercase							=						self.fine_acoustics_config.to_dict()

           __lowercase							=						self.__class__.model_type
           return output




