






"""simple docstring"""

import copy

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


lowercase__          = logging.get_logger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				int            =							'''encoder-decoder'''
		a_	:				List[str]            =							True





		def __init__(   self							:	Tuple						,			**a_							:	str	):
				super().__init__(**SCREAMING_SNAKE_CASE__	)
				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_   :							Tuple									=			kwargs.pop("decoder"	)
				lowerCAmelCase_   :							List[Any]									=			decoder_config.pop("model_type"	)

				from ..auto.configuration_auto import AutoConfig

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





		@classmethod
		def    lowerCamelCase       (   cls							:	int						,			a_							:	Optional[int]						,			a_							:	List[str]						,			**a_							:	str	):
				logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"	)
				lowerCAmelCase_   :							int									=			True
				lowerCAmelCase_   :							Tuple									=			True

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





		def    lowerCamelCase       (   self							:	int	):
				lowerCAmelCase_   :							Tuple									=			copy.deepcopy(self.__dict__	)
				lowerCAmelCase_   :							Union[str, Any]									=			self.encoder.to_dict()
				lowerCAmelCase_   :							int									=			self.decoder.to_dict()
				lowerCAmelCase_   :							str									=			self.__class__.model_type
				return output







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

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

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