





"""simple docstring"""


import copy

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


a__       :  Tuple       		=					logging.get_logger(__name__)

a__       :  Optional[int]       		=					{
    """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   lowercase_					(  _UpperCAmelCase				):
       snake_case__					:				Union[str, Any]									=  "musicgen_decoder"
       snake_case__					:				Optional[Any]									=  ["past_key_values"]

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=2048 ,       __SCREAMING_SNAKE_CASE=2048 ,       __SCREAMING_SNAKE_CASE=24 ,       __SCREAMING_SNAKE_CASE=4096 ,       __SCREAMING_SNAKE_CASE=16 ,       __SCREAMING_SNAKE_CASE=0.0 ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=1024 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.0 ,       __SCREAMING_SNAKE_CASE=0.0 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=4 ,       __SCREAMING_SNAKE_CASE=2048 ,       __SCREAMING_SNAKE_CASE=2048 ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE=False ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					vocab_size
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					hidden_size
        snake_case__     				=					ffn_dim
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					dropout
        snake_case__     				=					attention_dropout
        snake_case__     				=					activation_dropout
        snake_case__     				=					activation_function
        snake_case__     				=					initializer_factor
        snake_case__     				=					layerdrop
        snake_case__     				=					use_cache
        snake_case__     				=					scale_embedding  # scale factor will be sqrt(d_model) if True
        snake_case__     				=					num_codebooks
        super().__init__(
            pad_token_id=UpperCAmelCase_ ,       bos_token_id=UpperCAmelCase_ ,       eos_token_id=UpperCAmelCase_ ,       tie_word_embeddings=UpperCAmelCase_ ,       **UpperCAmelCase_ ,       )



class   lowercase_					(  _UpperCAmelCase				):
       snake_case__					:				Optional[Any]									=  "musicgen"
       snake_case__					:				Optional[Any]									=  True

       def __init__(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        super().__init__(**UpperCAmelCase_  )
        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"  )

        snake_case__     				=					kwargs.pop("text_encoder"  )
        snake_case__     				=					text_encoder_config.pop("model_type"  )

        snake_case__     				=					kwargs.pop("audio_encoder"  )
        snake_case__     				=					audio_encoder_config.pop("model_type"  )

        snake_case__     				=					kwargs.pop("decoder"  )

        snake_case__     				=					AutoConfig.for_model(UpperCAmelCase_ ,       **UpperCAmelCase_  )
        snake_case__     				=					AutoConfig.for_model(UpperCAmelCase_ ,       **UpperCAmelCase_  )
        snake_case__     				=					MusicgenDecoderConfig(**UpperCAmelCase_  )
        snake_case__     				=					True

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        return cls(
            text_encoder=text_encoder_config.to_dict() ,       audio_encoder=audio_encoder_config.to_dict() ,       decoder=decoder_config.to_dict() ,       **UpperCAmelCase_ ,       )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					copy.deepcopy(self.__dict__  )
        snake_case__     				=					self.text_encoder.to_dict()
        snake_case__     				=					self.audio_encoder.to_dict()
        snake_case__     				=					self.decoder.to_dict()
        snake_case__     				=					self.__class__.model_type
        return output









import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class  __UpperCAmelCase						(_UpperCAmelCase       ):







			def __init__(      self:    Optional[int] ,      UpperCAmelCase_:    NestedDataStructureLike[PathLike] ,      UpperCAmelCase_:    Optional[NamedSplit] = None ,      UpperCAmelCase_:    Optional[Features] = None ,      UpperCAmelCase_:    str = None ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Union[str, Any] ,      ):







									'''simple docstring'''




									super().__init__(
									    UpperCAmelCase_ ,      split=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      cache_dir=UpperCAmelCase_ ,      keep_in_memory=UpperCAmelCase_ ,      streaming=UpperCAmelCase_ ,      num_proc=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )
									_SCREAMING_SNAKE_CASE       =    path_or_paths if isinstance(UpperCAmelCase_ ,      UpperCAmelCase_	) else {self.split: path_or_paths}
									_SCREAMING_SNAKE_CASE       =    Csv(
									    cache_dir=UpperCAmelCase_ ,      data_files=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									if self.streaming:
															_SCREAMING_SNAKE_CASE       =    self.builder.as_streaming_dataset(split=self.split	)
									# Build regular (map-style) dataset
									else:
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None

															self.builder.download_and_prepare(
															    download_config=UpperCAmelCase_ ,      download_mode=UpperCAmelCase_ ,      verification_mode=UpperCAmelCase_ ,      base_path=UpperCAmelCase_ ,      num_proc=self.num_proc ,      )
															_SCREAMING_SNAKE_CASE       =    self.builder.as_dataset(
															    split=self.split ,      verification_mode=UpperCAmelCase_ ,      in_memory=self.keep_in_memory	)
									return dataset



class  __UpperCAmelCase						:







			def __init__(      self:    List[str] ,      UpperCAmelCase_:    Dataset ,      UpperCAmelCase_:    Union[PathLike, BinaryIO] ,      UpperCAmelCase_:    Optional[int] = None ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Any ,      ):







									'''simple docstring'''




									if num_proc is not None and num_proc <= 0:
															raise ValueError(F'num_proc {num_proc} must be an integer > 0.'	)

									_SCREAMING_SNAKE_CASE       =    dataset
									_SCREAMING_SNAKE_CASE       =    path_or_buf
									_SCREAMING_SNAKE_CASE       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_SCREAMING_SNAKE_CASE       =    num_proc
									_SCREAMING_SNAKE_CASE       =    """utf-8"""
									_SCREAMING_SNAKE_CASE       =    to_csv_kwargs







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""path_or_buf""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""header""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""index""" ,      UpperCAmelCase_	)

									if isinstance(self.path_or_buf ,      (str, bytes, os.PathLike)	):
															with open(self.path_or_buf ,      """wb+"""	) as buffer:
																					_SCREAMING_SNAKE_CASE       =    self._write(file_obj=UpperCAmelCase_ ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									else:
															_SCREAMING_SNAKE_CASE       =    self._write(file_obj=self.path_or_buf ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									return written







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    args

									_SCREAMING_SNAKE_CASE       =    query_table(
									    table=self.dataset.data ,      key=slice(UpperCAmelCase_ ,      offset + self.batch_size	) ,      indices=self.dataset._indices ,      )
									_SCREAMING_SNAKE_CASE       =    batch.to_pandas().to_csv(
									    path_or_buf=UpperCAmelCase_ ,      header=header if (offset == 0) else False ,      index=UpperCAmelCase_ ,      **UpperCAmelCase_	)
									return csv_str.encode(self.encoding	)







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    BinaryIO ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    0

									if self.num_proc is None or self.num_proc == 1:
															for offset in logging.tqdm(
															    range(0 ,      len(self.dataset	) ,      self.batch_size	) ,      unit="""ba""" ,      disable=not logging.is_progress_bar_enabled() ,      desc="""Creating CSV from Arrow format""" ,      ):
																					_SCREAMING_SNAKE_CASE       =    self._batch_csv((offset, header, index, to_csv_kwargs)	)
																					written += file_obj.write(UpperCAmelCase_	)

									else:
															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    len(self.dataset	), self.batch_size
															with multiprocessing.Pool(self.num_proc	) as pool:
																					for csv_str in logging.tqdm(
																					    pool.imap(
																					        self._batch_csv ,      [(offset, header, index, to_csv_kwargs) for offset in range(0 ,      UpperCAmelCase_ ,      UpperCAmelCase_	)] ,      ) ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,      unit="""ba""" ,      disable=not logging.is_progress_bar_enabled() ,      desc="""Creating CSV from Arrow format""" ,      ):
																											written += file_obj.write(UpperCAmelCase_	)

									return written

