





"""simple docstring"""
import copy
import os
from typing import Union

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


_snake_case     :		Any       						= logging.get_logger(__name__)

_snake_case     :		List[str]       						= {
    '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 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''bridgetower_vision_model'''


					def __init__(					self			:Tuple     ,       __UpperCamelCase			:List[str]=7_68     ,       __UpperCamelCase			:Optional[int]=12     ,       __UpperCamelCase			:List[str]=3     ,       __UpperCamelCase			:Optional[Any]=16     ,       __UpperCamelCase			:Union[str, Any]=2_88     ,       __UpperCamelCase			:List[str]=1     ,       __UpperCamelCase			:Dict=1e-05     ,       __UpperCamelCase			:Tuple=False     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:Any=False     ,       **__UpperCamelCase			:Optional[int]     ,       ):
												super().__init__(**__UpperCamelCase		)
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_channels
												A		=  patch_size
												A		=  image_size
												A		=  initializer_factor
												A		=  layer_norm_eps
												A		=  stop_gradient
												A		=  share_layernorm
												A		=  remove_last_layer


					@classmethod
					def 	lowerCamelCase       (					cls			:int     ,       __UpperCamelCase			:Union[str, os.PathLike]     ,       **__UpperCamelCase			:List[str]		):
												A,	A		=  cls.get_config_dict(__UpperCamelCase     ,       **__UpperCamelCase		)

												if config_dict.get("model_type"		) == "bridgetower":
																			A		=  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(__UpperCamelCase     ,       **__UpperCamelCase		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''bridgetower_text_model'''


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:Any=5_02_65     ,       __UpperCamelCase			:List[Any]=7_68     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:Optional[Any]=12     ,       __UpperCamelCase			:str=1     ,       __UpperCamelCase			:Optional[Any]=30_72     ,       __UpperCamelCase			:Tuple="gelu"     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Tuple=0.1     ,       __UpperCamelCase			:str=5_14     ,       __UpperCamelCase			:List[Any]=1     ,       __UpperCamelCase			:Dict=1e-05     ,       __UpperCamelCase			:Dict=1     ,       __UpperCamelCase			:str=0     ,       __UpperCamelCase			:Tuple=2     ,       __UpperCamelCase			:str="absolute"     ,       __UpperCamelCase			:Union[str, Any]=True     ,       **__UpperCamelCase			:Any     ,       ):
												super().__init__(**__UpperCamelCase		)

												A		=  vocab_size
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  hidden_act
												A		=  initializer_factor
												A		=  intermediate_size
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  max_position_embeddings
												A		=  type_vocab_size
												A		=  layer_norm_eps
												A		=  position_embedding_type
												A		=  use_cache
												A		=  pad_token_id
												A		=  bos_token_id
												A		=  eos_token_id


					@classmethod
					def 	lowerCamelCase       (					cls			:Dict     ,       __UpperCamelCase			:Union[str, os.PathLike]     ,       **__UpperCamelCase			:Tuple		):
												A,	A		=  cls.get_config_dict(__UpperCamelCase     ,       **__UpperCamelCase		)

												if config_dict.get("model_type"		) == "bridgetower":
																			A		=  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(__UpperCamelCase     ,       **__UpperCamelCase		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''bridgetower'''


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:int="gelu"     ,       __UpperCamelCase			:Union[str, Any]=7_68     ,       __UpperCamelCase			:int=1     ,       __UpperCamelCase			:List[Any]=1e-05     ,       __UpperCamelCase			:Optional[Any]=False     ,       __UpperCamelCase			:Dict="add"     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:Optional[Any]=6     ,       __UpperCamelCase			:Optional[int]=False     ,       __UpperCamelCase			:List[str]=False     ,       __UpperCamelCase			:str=None     ,       __UpperCamelCase			:Any=None     ,       **__UpperCamelCase			:Dict     ,       ):
												# TODO: remove this once the Hub files are updated.
												A		=  kwargs.pop("text_config_dict"     ,       __UpperCamelCase		)
												A		=  kwargs.pop("vision_config_dict"     ,       __UpperCamelCase		)

												super().__init__(**__UpperCamelCase		)
												A		=  share_cross_modal_transformer_layers
												A		=  hidden_act
												A		=  hidden_size
												A		=  initializer_factor
												A		=  layer_norm_eps
												A		=  share_link_tower_layers
												A		=  link_tower_type
												A		=  num_attention_heads
												A		=  num_hidden_layers
												A		=  tie_word_embeddings
												A		=  init_layernorm_from_vision_encoder

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

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

												A		=  BridgeTowerTextConfig(**__UpperCamelCase		)
												A		=  BridgeTowerVisionConfig(**__UpperCamelCase		)


					@classmethod
					def 	lowerCamelCase       (					cls			:str     ,       __UpperCamelCase			:BridgeTowerTextConfig     ,       __UpperCamelCase			:BridgeTowerVisionConfig     ,       **__UpperCamelCase			:int		):
												return cls(text_config=text_config.to_dict()     ,       vision_config=vision_config.to_dict()     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  copy.deepcopy(self.__dict__		)
												A		=  self.text_config.to_dict()
												A		=  self.vision_config.to_dict()
												A		=  self.__class__.model_type
												return output









"""simple docstring"""
def 					A__				(			UpperCamelCase					,  UpperCamelCase = False       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														A		=  F"Expected string as input, found {type(UpperCamelCase       )}"
														raise ValueError(UpperCamelCase       )
							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														A		=  F"Expected boolean as use_pascal parameter, found {type(UpperCamelCase       )}"
														raise ValueError(UpperCamelCase       )

							A		=  input_str.split("_"       )

							A		=  0 if use_pascal else 1

							A		=  words[start_index:]

							A		=  [word[0].upper() + word[1:] for word in words_to_capitalize]

							A		=  "" if use_pascal else words[0]

							return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
		from doctest import testmod

		testmod()


