code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
      # See all Musicgen models at https://huggingface.co/models?filter=musicgen
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Tuple							= '''musicgen_decoder'''
  			A	:							str							= ['''past_key_values''']
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : Dict=2_048 ,						lowerCAmelCase_       : Dict=2_048 ,						lowerCAmelCase_       : Optional[Any]=24 ,						lowerCAmelCase_       : Any=4_096 ,						lowerCAmelCase_       : Any=16 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : List[str]=True ,						lowerCAmelCase_       : int="gelu" ,						lowerCAmelCase_       : int=1_024 ,						lowerCAmelCase_       : Union[str, Any]=0.1 ,						lowerCAmelCase_       : List[str]=0.0 ,						lowerCAmelCase_       : Optional[int]=0.0 ,						lowerCAmelCase_       : List[str]=0.02 ,						lowerCAmelCase_       : Tuple=False ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : List[Any]=2_048 ,						lowerCAmelCase_       : Optional[int]=2_048 ,						lowerCAmelCase_       : Tuple=None ,						lowerCAmelCase_       : str=False ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						__A=  vocab_size
  						__A=  max_position_embeddings
  						__A=  hidden_size
  						__A=  ffn_dim
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  dropout
  						__A=  attention_dropout
  						__A=  activation_dropout
  						__A=  activation_function
  						__A=  initializer_factor
  						__A=  layerdrop
  						__A=  use_cache
  						__A=  scale_embedding  # scale factor will be sqrt(d_model) if True
  						__A=  num_codebooks
  						super().__init__(
  						    pad_token_id=lowerCAmelCase_ ,						bos_token_id=lowerCAmelCase_ ,						eos_token_id=lowerCAmelCase_ ,						tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_ ,						)
  
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							List[str]							= '''musicgen'''
  			A	:							Optional[Any]							= True
  			def __init__(     self       : Dict ,						**lowerCAmelCase_       : Optional[Any]  )			->		List[str]:
  						super().__init__(**lowerCAmelCase_  )
  						if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
  									raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'  )
  
  						__A=  kwargs.pop('text_encoder'  )
  						__A=  text_encoder_config.pop('model_type'  )
  
  						__A=  kwargs.pop('audio_encoder'  )
  						__A=  audio_encoder_config.pop('model_type'  )
  
  						__A=  kwargs.pop('decoder'  )
  
  						__A=  AutoConfig.for_model(lowerCAmelCase_ ,						**lowerCAmelCase_  )
  						__A=  AutoConfig.for_model(lowerCAmelCase_ ,						**lowerCAmelCase_  )
  						__A=  MusicgenDecoderConfig(**lowerCAmelCase_  )
  						__A=  True
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Any ,						lowerCAmelCase_       : PretrainedConfig ,						lowerCAmelCase_       : PretrainedConfig ,						lowerCAmelCase_       : MusicgenDecoderConfig ,						**lowerCAmelCase_       : int ,						)			->		Tuple:
  						return cls(
  						    text_encoder=text_encoder_config.to_dict() ,						audio_encoder=audio_encoder_config.to_dict() ,						decoder=decoder_config.to_dict() ,						**lowerCAmelCase_ ,						)
  
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
  						__A=  copy.deepcopy(self.__dict__  )
  						__A=  self.text_encoder.to_dict()
  						__A=  self.audio_encoder.to_dict()
  						__A=  self.decoder.to_dict()
  						__A=  self.__class__.model_type
  						return output
  
  
  
  
style_context: 
  
  
  '''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 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,**_SCREAMING_SNAKE_CASE				:   Tuple   ):
  
  
  
  
  
  
  			"""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,**_SCREAMING_SNAKE_CASE,)
  
  
  
  
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@staticmethod
  			def 				lowerCAmelCase    (     lowerCAmelCase_       : ArgumentParser  )			->		Tuple:
  						__A=  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       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						**lowerCAmelCase_       : Tuple ,						)			->		List[Any]:
  						__A=  dataset
  						__A=  name
  						__A=  cache_dir
  						__A=  beam_pipeline_options
  						__A=  data_dir
  						__A=  all_configs
  						__A=  save_infos
  						__A=  ignore_verifications
  						__A=  force_redownload
  						__A=  config_kwargs
  
  			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Optional[Any]:
  						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  )
  						__A,  __A=  self._dataset, self._name
  						__A=  dataset_module_factory(lowerCAmelCase_  )
  						__A=  import_main_class(dataset_module.module_path  )
  						__A=  []
  						if self._beam_pipeline_options:
  									__A=  beam.options.pipeline_options.PipelineOptions(
  									    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','  ) if opt]  )
  						else:
  									__A=  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:
  									__A=  os.path.join(builder_cls.get_imported_module_dir() ,						config.DATASETDICT_INFOS_FILENAME  )
  
  									__A=  Path(lowerCAmelCase_  ).name + '.py'
  
  									__A=  os.path.join(lowerCAmelCase_ ,						lowerCAmelCase_  )
  									if os.path.isfile(lowerCAmelCase_  ):
  												__A=  os.path.dirname(lowerCAmelCase_  )
  									elif os.path.isfile(lowerCAmelCase_  ):
  												__A=  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
  									__A=  os.path.join(lowerCAmelCase_ ,						config.DATASETDICT_INFOS_FILENAME  )
  									copyfile(lowerCAmelCase_ ,						lowerCAmelCase_  )
  									print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""  )
  
  
  
  