




import copy

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


__lowerCAmelCase						:							List[Any]			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							str			     =						{
    """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__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Any									=   """musicgen_decoder"""
				SCREAMING_SNAKE_CASE_			:				Dict									=   ["""past_key_values"""]





				def __init__( self							:	int   , __lowerCamelCase							:	int=20_48   , __lowerCamelCase							:	List[Any]=20_48   , __lowerCamelCase							:	List[str]=24   , __lowerCamelCase							:	Union[str, Any]=40_96   , __lowerCamelCase							:	Tuple=16   , __lowerCamelCase							:	Dict=0.0   , __lowerCamelCase							:	List[str]=True   , __lowerCamelCase							:	List[str]="gelu"   , __lowerCamelCase							:	List[str]=10_24   , __lowerCamelCase							:	Dict=0.1   , __lowerCamelCase							:	Optional[Any]=0.0   , __lowerCamelCase							:	Tuple=0.0   , __lowerCamelCase							:	str=0.02   , __lowerCamelCase							:	Union[str, Any]=False   , __lowerCamelCase							:	Optional[Any]=4   , __lowerCamelCase							:	Optional[int]=20_48   , __lowerCamelCase							:	List[Any]=20_48   , __lowerCamelCase							:	Dict=None   , __lowerCamelCase							:	int=False   , **__lowerCamelCase							:	Optional[Any]   , )   ->	str:
							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=a_   , bos_token_id=a_   , eos_token_id=a_   , tie_word_embeddings=a_   , **a_   , )




class        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   """musicgen"""
				SCREAMING_SNAKE_CASE_			:				int									=   True





				def __init__( self							:	Optional[int]   , **__lowerCamelCase							:	int				)   ->	str:
							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"				)

							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(a_   , **a_				)
							a       =     AutoConfig.for_model(a_   , **a_				)
							a       =     MusicgenDecoderConfig(**a_				)
							a       =     True





				@classmethod
				def 				__UpperCAmelCase				( cls							:	Tuple   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Optional[Any]   , **__lowerCamelCase							:	int   , )   ->	Tuple:
							return cls(
							    text_encoder=text_encoder_config.to_dict()   , audio_encoder=audio_encoder_config.to_dict()   , decoder=decoder_config.to_dict()   , **a_   , )





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							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










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							["""BartTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       List[Any]							   =							[
			    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BartForCausalLM""",
			    """BartForConditionalGeneration""",
			    """BartForQuestionAnswering""",
			    """BartForSequenceClassification""",
			    """BartModel""",
			    """BartPretrainedModel""",
			    """PretrainedBartModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							[
			    """TFBartForConditionalGeneration""",
			    """TFBartForSequenceClassification""",
			    """TFBartModel""",
			    """TFBartPretrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       str							   =							[
			    """FlaxBartDecoderPreTrainedModel""",
			    """FlaxBartForCausalLM""",
			    """FlaxBartForConditionalGeneration""",
			    """FlaxBartForQuestionAnswering""",
			    """FlaxBartForSequenceClassification""",
			    """FlaxBartModel""",
			    """FlaxBartPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
			from .tokenization_bart import BartTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bart_fast import BartTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bart import (
						    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BartForCausalLM,
						    BartForConditionalGeneration,
						    BartForQuestionAnswering,
						    BartForSequenceClassification,
						    BartModel,
						    BartPretrainedModel,
						    PretrainedBartModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_bart import (
						    TFBartForConditionalGeneration,
						    TFBartForSequenceClassification,
						    TFBartModel,
						    TFBartPretrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_bart import (
						    FlaxBartDecoderPreTrainedModel,
						    FlaxBartForCausalLM,
						    FlaxBartForConditionalGeneration,
						    FlaxBartForQuestionAnswering,
						    FlaxBartForSequenceClassification,
						    FlaxBartModel,
						    FlaxBartPreTrainedModel,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE					:       Tuple							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





