


from typing import TYPE_CHECKING

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


_UpperCAmelCase			:  int     ={
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase			:  Optional[int]     =["""LxmertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase			:  int     =[
          """LxmertEncoder""",
          """LxmertForPreTraining""",
          """LxmertForQuestionAnswering""",
          """LxmertModel""",
          """LxmertPreTrainedModel""",
          """LxmertVisualFeatureEncoder""",
          """LxmertXLayer""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase			:  Tuple     =[
          """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFLxmertForPreTraining""",
          """TFLxmertMainLayer""",
          """TFLxmertModel""",
          """TFLxmertPreTrainedModel""",
          """TFLxmertVisualFeatureEncoder""",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      _UpperCAmelCase			:  Optional[Any]     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



import copy

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


_UpperCAmelCase			:  str     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							"""encoder-decoder"""
						SCREAMING_SNAKE_CASE__      :  str					=							True




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

										from ..auto.configuration_auto import AutoConfig

										lowerCAmelCase_ :      Union[str, Any]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Any    				=				True




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase   ,						__lowercase   ,						**__lowercase							)					->    PretrainedConfig:
										logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''							)
										lowerCAmelCase_ :      int    				=				True
										lowerCAmelCase_ :      List[Any]    				=				True

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




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[Any]    				=				copy.deepcopy(self.__dict__							)
										lowerCAmelCase_ :      List[str]    				=				self.encoder.to_dict()
										lowerCAmelCase_ :      Dict    				=				self.decoder.to_dict()
										lowerCAmelCase_ :      Optional[Any]    				=				self.__class__.model_type
										return output