
import copy

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


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

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Dict        =		"""encoder-decoder"""
		a__       :						Optional[int]        =		True






		def __init__(		self		:							Union[str, Any]							,			**SCREAMING_SNAKE_CASE__		:							Any  )     ->							Optional[int]:
						super().__init__(**SCREAMING_SNAKE_CASE__  )
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						__lowerCamelCase    					=	kwargs.pop('''encoder'''  )
						__lowerCamelCase    					=	encoder_config.pop('''model_type'''  )
						__lowerCamelCase    					=	kwargs.pop('''decoder'''  )
						__lowerCamelCase    					=	decoder_config.pop('''model_type'''  )

						from ..auto.configuration_auto import AutoConfig

						__lowerCamelCase    					=	AutoConfig.for_model(SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	AutoConfig.for_model(SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	True






		@classmethod
		def  __A       (		cls		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							Dict  )     ->							PretrainedConfig:
						logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	True

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







		def  __A       (		self		:							Any  )     ->							Dict:
						__lowerCamelCase    					=	copy.deepcopy(self.__dict__  )
						__lowerCamelCase    					=	self.encoder.to_dict()
						__lowerCamelCase    					=	self.decoder.to_dict()
						__lowerCamelCase    					=	self.__class__.model_type
						return output





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



