






import copy

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


A__      :     int       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""encoder-decoder"""
				lowercase__											=			True




				def __init__(	self		:       Union[str, Any],    **lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )
								assert (
								    "encoder" in kwargs and "decoder" in kwargs
								), "Config has to be initialized with encoder and decoder config"
								lowercase__  					=     kwargs.pop('''encoder'''    )
								lowercase__  					=     encoder_config.pop('''model_type'''    )
								lowercase__  					=     kwargs.pop('''decoder'''    )
								lowercase__  					=     decoder_config.pop('''model_type'''    )

								from ..auto.configuration_auto import AutoConfig

								lowercase__  					=     AutoConfig.for_model(lowerCamelCase,    **lowerCamelCase    )
								lowercase__  					=     AutoConfig.for_model(lowerCamelCase,    **lowerCamelCase    )
								lowercase__  					=     True




				@classmethod
				def 							lowercase__  (	cls		:       Optional[int],    lowerCamelCase		:       PretrainedConfig,    lowerCamelCase		:       PretrainedConfig,    **lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''    )
								lowercase__  					=     True
								lowercase__  					=     True

								return cls(encoder=encoder_config.to_dict(),    decoder=decoder_config.to_dict(),    **lowerCamelCase    )




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     copy.deepcopy(self.__dict__    )
								lowercase__  					=     self.encoder.to_dict()
								lowercase__  					=     self.decoder.to_dict()
								lowercase__  					=     self.__class__.model_type
								return output












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

							A__      :     int       =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




