



import copy

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


A__       =			logging.get_logger(__name__)

A__       =			{
    """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    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''musicgen_decoder'''
				__lowerCamelCase					=							['''past_key_values''']




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



									"""simple docstring"""





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





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''musicgen'''
				__lowerCamelCase					=							True




				def __init__(				self		,							**_snake_case						):



									"""simple docstring"""





									super().__init__(**SCREAMING_SNAKE_CASE_						)
									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"""						)

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

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

									_lowerCAmelCase         =       kwargs.pop("""decoder"""						)

									_lowerCAmelCase         =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       AutoConfig.for_model(SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       True




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							_snake_case		,							_snake_case		,							**_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()		,							**SCREAMING_SNAKE_CASE_		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





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





'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

