



"""simple docstring"""




import copy

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


_lowerCAmelCase       :      Tuple							=							logging.get_logger(__name__)

_lowerCAmelCase       :      Optional[Any]							=							{
    "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 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				'musicgen_decoder'
      __SCREAMING_SNAKE_CASE		:     Dict				  =				['past_key_values']



      def __init__(  self  :			Optional[int]  ,			A  :			int=2_0_4_8  ,			A  :			Tuple=2_0_4_8  ,			A  :			Optional[Any]=2_4  ,			A  :			Tuple=4_0_9_6  ,			A  :			Tuple=1_6  ,			A  :			Union[str, Any]=0.0  ,			A  :			List[Any]=True  ,			A  :			int="gelu"  ,			A  :			Union[str, Any]=1_0_2_4  ,			A  :			str=0.1  ,			A  :			Optional[Any]=0.0  ,			A  :			Dict=0.0  ,			A  :			str=0.02  ,			A  :			List[str]=False  ,			A  :			Optional[int]=4  ,			A  :			List[str]=2_0_4_8  ,			A  :			Optional[Any]=2_0_4_8  ,			A  :			Tuple=None  ,			A  :			Optional[Any]=False  ,			**A  :			str  ,			):
           _UpperCAmelCase    :			Optional[int]              =					vocab_size
           _UpperCAmelCase    :			Any              =					max_position_embeddings
           _UpperCAmelCase    :			Optional[int]              =					hidden_size
           _UpperCAmelCase    :			Optional[Any]              =					ffn_dim
           _UpperCAmelCase    :			List[Any]              =					num_hidden_layers
           _UpperCAmelCase    :			int              =					num_attention_heads
           _UpperCAmelCase    :			int              =					dropout
           _UpperCAmelCase    :			List[str]              =					attention_dropout
           _UpperCAmelCase    :			Optional[int]              =					activation_dropout
           _UpperCAmelCase    :			Any              =					activation_function
           _UpperCAmelCase    :			Optional[Any]              =					initializer_factor
           _UpperCAmelCase    :			Optional[int]              =					layerdrop
           _UpperCAmelCase    :			Dict              =					use_cache
           _UpperCAmelCase    :			int              =					scale_embedding  # scale factor will be sqrt(d_model) if True
           _UpperCAmelCase    :			Optional[int]              =					num_codebooks
           super().__init__(
               pad_token_id=A  ,			bos_token_id=A  ,			eos_token_id=A  ,			tie_word_embeddings=A  ,			**A  ,			)







class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				'musicgen'
      __SCREAMING_SNAKE_CASE		:     Dict				  =				True



      def __init__(  self  :			int  ,			**A  :			Any		):
           super().__init__(**A		)
           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"		)

           _UpperCAmelCase    :			List[str]              =					kwargs.pop("text_encoder"		)
           _UpperCAmelCase    :			List[str]              =					text_encoder_config.pop("model_type"		)

           _UpperCAmelCase    :			Union[str, Any]              =					kwargs.pop("audio_encoder"		)
           _UpperCAmelCase    :			Any              =					audio_encoder_config.pop("model_type"		)

           _UpperCAmelCase    :			Optional[int]              =					kwargs.pop("decoder"		)

           _UpperCAmelCase    :			List[str]              =					AutoConfig.for_model(A  ,			**A		)
           _UpperCAmelCase    :			Tuple              =					AutoConfig.for_model(A  ,			**A		)
           _UpperCAmelCase    :			Any              =					MusicgenDecoderConfig(**A		)
           _UpperCAmelCase    :			Union[str, Any]              =					True



      @classmethod
      def 		snake_case_      (  cls  :			int  ,			A  :			PretrainedConfig  ,			A  :			PretrainedConfig  ,			A  :			MusicgenDecoderConfig  ,			**A  :			Tuple  ,			):
           return cls(
               text_encoder=text_encoder_config.to_dict()  ,			audio_encoder=audio_encoder_config.to_dict()  ,			decoder=decoder_config.to_dict()  ,			**A  ,			)



      def 		snake_case_      (  self  :			int		):
           _UpperCAmelCase    :			Optional[int]              =					copy.deepcopy(self.__dict__		)
           _UpperCAmelCase    :			List[Any]              =					self.text_encoder.to_dict()
           _UpperCAmelCase    :			str              =					self.audio_encoder.to_dict()
           _UpperCAmelCase    :			List[str]              =					self.decoder.to_dict()
           _UpperCAmelCase    :			str              =					self.__class__.model_type
           return output





"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     bool:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 1 and series[0] != 0:
          return True
     _UpperCAmelCase    :			Tuple              =					[]
     _UpperCAmelCase    :			Any              =					len(SCREAMING_SNAKE_CASE__       )
     for i in range(0		,						SCREAMING_SNAKE_CASE__       ):
          if series[i] == 0:
               raise ValueError("Input series cannot have 0 as an element"       )
          rec_series.append(1 / series[i]       )
     _UpperCAmelCase    :			Dict              =					rec_series[1] - rec_series[0]
     for index in range(2		,						SCREAMING_SNAKE_CASE__       ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     float:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [2, 4, 6]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     _UpperCAmelCase    :			Any              =					0
     for val in series:
          answer += 1 / val
     return len(SCREAMING_SNAKE_CASE__       ) / answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
