






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase__     :   List[str]			 =	{
    "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
}




class        snake_case_					(						SCREAMING_SNAKE_CASE__			):

						"""simple docstring"""



						snake_case__   					=			'regnet'
						snake_case__   					=			['x', 'y']



						def __init__(self:				Optional[int]   ,				__UpperCAmelCase:				Union[str, Any]=3   ,				__UpperCAmelCase:				Optional[Any]=32   ,				__UpperCAmelCase:				Union[str, Any]=[128, 192, 512, 1088]   ,				__UpperCAmelCase:				Union[str, Any]=[2, 6, 12, 2]   ,				__UpperCAmelCase:				Optional[Any]=64   ,				__UpperCAmelCase:				Tuple="y"   ,				__UpperCAmelCase:				Any="relu"   ,				**__UpperCAmelCase:				List[str]   ,				)						->    List[str]:




											'''simple docstring'''



											super().__init__(**a_      )
											if layer_type not in self.layer_types:
																raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types      )}'      )
											__a				:	Optional[Any]			=				num_channels
											__a				:	Optional[int]			=				embedding_size
											__a				:	Optional[Any]			=				hidden_sizes
											__a				:	Tuple			=				depths
											__a				:	Any			=				groups_width
											__a				:	Any			=				layer_type
											__a				:	Any			=				hidden_act
											# always downsample in the first stage
											__a				:	Optional[int]			=				True













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        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen_decoder"""
						snake_case__   					=			["""past_key_values"""]



						def __init__(self:				str   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				Union[str, Any]=2048   ,				__UpperCAmelCase:				List[Any]=24   ,				__UpperCAmelCase:				Dict=4096   ,				__UpperCAmelCase:				int=16   ,				__UpperCAmelCase:				Dict=0.0   ,				__UpperCAmelCase:				str=True   ,				__UpperCAmelCase:				Tuple="gelu"   ,				__UpperCAmelCase:				Optional[Any]=1024   ,				__UpperCAmelCase:				Optional[Any]=0.1   ,				__UpperCAmelCase:				List[Any]=0.0   ,				__UpperCAmelCase:				int=0.0   ,				__UpperCAmelCase:				List[Any]=0.02   ,				__UpperCAmelCase:				Union[str, Any]=False   ,				__UpperCAmelCase:				Any=4   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Tuple=2048   ,				__UpperCAmelCase:				Dict=None   ,				__UpperCAmelCase:				Dict=False   ,				**__UpperCAmelCase:				Any   ,				)						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Dict			=				vocab_size
											__a				:	List[str]			=				max_position_embeddings
											__a				:	int			=				hidden_size
											__a				:	Union[str, Any]			=				ffn_dim
											__a				:	List[Any]			=				num_hidden_layers
											__a				:	List[Any]			=				num_attention_heads
											__a				:	List[Any]			=				dropout
											__a				:	str			=				attention_dropout
											__a				:	Union[str, Any]			=				activation_dropout
											__a				:	Optional[int]			=				activation_function
											__a				:	List[str]			=				initializer_factor
											__a				:	Union[str, Any]			=				layerdrop
											__a				:	Optional[int]			=				use_cache
											__a				:	List[Any]			=				scale_embedding  # scale factor will be sqrt(d_model) if True
											__a				:	str			=				num_codebooks
											super().__init__(
											    pad_token_id=__UpperCAmelCase   ,				bos_token_id=__UpperCAmelCase   ,				eos_token_id=__UpperCAmelCase   ,				tie_word_embeddings=__UpperCAmelCase   ,				**__UpperCAmelCase   ,				)




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			"""musicgen"""
						snake_case__   					=			True



						def __init__(self:				Union[str, Any]   ,				**__UpperCAmelCase:				Optional[int]      )						->    List[str]:




											'''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"      )

											__a				:	List[Any]			=				kwargs.pop("text_encoder"      )
											__a				:	Optional[Any]			=				text_encoder_config.pop("model_type"      )

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

											__a				:	Optional[Any]			=				kwargs.pop("decoder"      )

											__a				:	Optional[int]			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	int			=				AutoConfig.for_model(__UpperCAmelCase   ,				**__UpperCAmelCase      )
											__a				:	List[Any]			=				MusicgenDecoderConfig(**__UpperCAmelCase      )
											__a				:	Optional[int]			=				True



						@classmethod
						def 					UpperCAmelCase__				(cls:				Union[str, Any]   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				PretrainedConfig   ,				__UpperCAmelCase:				MusicgenDecoderConfig   ,				**__UpperCAmelCase:				Any   ,				)						->    Tuple:




											'''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 					UpperCAmelCase__				(self:				List[str]      )						->    int:




											'''simple docstring'''



											__a				:	Dict			=				copy.deepcopy(self.__dict__      )
											__a				:	Optional[Any]			=				self.text_encoder.to_dict()
											__a				:	Optional[Any]			=				self.audio_encoder.to_dict()
											__a				:	Tuple			=				self.decoder.to_dict()
											__a				:	List[Any]			=				self.__class__.model_type
											return output





