
"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__      :    Optional[int]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Optional[Any]    							= {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   '''dpt'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=3_84   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=[2, 5, 8, 11]   ,       SCREAMING_SNAKE_CASE__="project"   ,       SCREAMING_SNAKE_CASE__=[4, 2, 1, 0.5]   ,       SCREAMING_SNAKE_CASE__=[96, 1_92, 3_84, 7_68]   ,       SCREAMING_SNAKE_CASE__=2_56   ,       SCREAMING_SNAKE_CASE__=-1   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=0.4   ,       SCREAMING_SNAKE_CASE__=2_55   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=[1, 10_24, 24, 24]   ,       SCREAMING_SNAKE_CASE__=[0, 1]   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   is_hybrid

											if self.is_hybrid:
																		if backbone_config is None:
																									logger.info("""Initializing the config with a `BiT` backbone."""    )
																									SCREAMING_SNAKE_CASE__						:       Any           =   {
																									    """global_padding""": """same""",
																									    """layer_type""": """bottleneck""",
																									    """depths""": [3, 4, 9],
																									    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																									    """embedding_dynamic_padding""": True,
																									}
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   BitConfig(**SCREAMING_SNAKE_CASE__    )
																		elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									logger.info("""Initializing the config with a `BiT` backbone."""    )
																									SCREAMING_SNAKE_CASE__						:       str           =   BitConfig(**SCREAMING_SNAKE_CASE__    )
																		elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   backbone_config
																		else:
																									raise ValueError(
																									    F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''    )

																		SCREAMING_SNAKE_CASE__						:       Dict           =   backbone_featmap_shape
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   neck_ignore_stages

																		if readout_type != "project":
																									raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""    )
											else:
																		SCREAMING_SNAKE_CASE__						:       str           =   None
																		SCREAMING_SNAKE_CASE__						:       int           =   None
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   []

											SCREAMING_SNAKE_CASE__						:       Any           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       List[str]           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       int           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[str]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Tuple           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Tuple           =   image_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_channels
											SCREAMING_SNAKE_CASE__						:       Any           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   backbone_out_indices
											if readout_type not in ["ignore", "add", "project"]:
																		raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   readout_type
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   reassemble_factors
											SCREAMING_SNAKE_CASE__						:       List[str]           =   neck_hidden_sizes
											SCREAMING_SNAKE_CASE__						:       Dict           =   fusion_hidden_size
											SCREAMING_SNAKE_CASE__						:       int           =   head_in_index
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   use_batch_norm_in_fusion_residual
											# auxiliary head attributes (semantic segmentation)
											SCREAMING_SNAKE_CASE__						:       Tuple           =   use_auxiliary_head
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   auxiliary_loss_weight
											SCREAMING_SNAKE_CASE__						:       Tuple           =   semantic_loss_ignore_index
											SCREAMING_SNAKE_CASE__						:       List[str]           =   semantic_classifier_dropout



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   copy.deepcopy(self.__dict__    )

											if output["backbone_config"] is not None:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.backbone_config.to_dict()

											SCREAMING_SNAKE_CASE__						:       str           =   self.__class__.model_type
											return output






import copy
import os
from typing import Union

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	str							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_act
       lowerCamelCase		:	Optional[int]							=      initializer_factor
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	Union[str, Any]							=      type_vocab_size
       lowerCamelCase		:	Optional[int]							=      layer_norm_eps
       lowerCamelCase		:	Optional[int]							=      position_embedding_type
       lowerCamelCase		:	List[str]							=      use_cache
       lowerCamelCase		:	List[str]							=      pad_token_id
       lowerCamelCase		:	List[str]							=      bos_token_id
       lowerCamelCase		:	Optional[int]							=      eos_token_id






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[int]							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




