"""simple docstring"""
import copy

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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)



class 					lowerCAmelCase_      (     __lowercase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  '''encoder-decoder'''
				_lowerCAmelCase       :      List[str]     					=  True

				def __init__(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

										from ..auto.configuration_auto import AutoConfig

										snake_case        =     AutoConfig.for_model(UpperCAmelCase__						,      **UpperCAmelCase__      )
										snake_case        =     AutoConfig.for_model(UpperCAmelCase__						,      **UpperCAmelCase__      )
										snake_case        =     True

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     copy.deepcopy(self.__dict__      )
										snake_case        =     self.encoder.to_dict()
										snake_case        =     self.decoder.to_dict()
										snake_case        =     self.__class__.model_type
										return output



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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


