


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case				:    Dict       				=     logging.get_logger(__name__)

snake_case				:    Any       				=     {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class 		_snake_case  (      snake_case_							):
 SCREAMING_SNAKE_CASE__      =       'roberta-prelayernorm'




 def __init__(		self					,  _lowerCamelCase=5_0265					,  _lowerCamelCase=768					,  _lowerCamelCase=12					,  _lowerCamelCase=12					,  _lowerCamelCase=3072					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=512					,  _lowerCamelCase=2					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-12					,  _lowerCamelCase=1					,  _lowerCamelCase=0					,  _lowerCamelCase=2					,  _lowerCamelCase="absolute"					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  **_lowerCamelCase					,  ):
       super().__init__(pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  **_lowerCamelCase    )

       a						:Dict 							=       vocab_size
       a						:Any 							=       hidden_size
       a						:int 							=       num_hidden_layers
       a						:Any 							=       num_attention_heads
       a						:int 							=       hidden_act
       a						:Dict 							=       intermediate_size
       a						:int 							=       hidden_dropout_prob
       a						:List[Any] 							=       attention_probs_dropout_prob
       a						:Any 							=       max_position_embeddings
       a						:Tuple 							=       type_vocab_size
       a						:Optional[int] 							=       initializer_range
       a						:Optional[int] 							=       layer_norm_eps
       a						:Union[str, Any] 							=       position_embedding_type
       a						:Tuple 							=       use_cache
       a						:str 							=       classifier_dropout







class 		_snake_case  (      snake_case_							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task == "multiple-choice":
             a						:str 							=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             a						:Any 							=       {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]    )












'''simple docstring'''

import copy
import os
from typing import Union

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """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_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_vision_model'''


    def __init__(		self ,		A=768 ,		A=12 ,		A=3 ,		A=16 ,		A=288 ,		A=1 ,		A=1e-05 ,		A=False ,		A=True ,		A=False ,		**A ,		)		->   Dict:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			stop_gradient
        _SCREAMING_SNAKE_CASE									=			share_layernorm
        _SCREAMING_SNAKE_CASE									=			remove_last_layer





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

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




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_text_model'''


    def __init__(		self ,		A=5_0265 ,		A=768 ,		A=12 ,		A=12 ,		A=1 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=514 ,		A=1 ,		A=1e-05 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		**A ,		)		->   Union[str, Any]:
        super().__init__(**A       )

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





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

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




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower'''


    def __init__(		self ,		A=True ,		A="gelu" ,		A=768 ,		A=1 ,		A=1e-05 ,		A=False ,		A="add" ,		A=12 ,		A=6 ,		A=False ,		A=False ,		A=None ,		A=None ,		**A ,		)		->   Tuple:
        # TODO: remove this once the Hub files are updated.
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_config_dict""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""vision_config_dict""" ,		A       )

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

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

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

        _SCREAMING_SNAKE_CASE									=			BridgeTowerTextConfig(**A       )
        _SCREAMING_SNAKE_CASE									=			BridgeTowerVisionConfig(**A       )


    @classmethod
    def 			snake_case_(		cls ,		A ,		A ,		**A       )		->   int:
        return cls(text_config=text_config.to_dict() ,		vision_config=vision_config.to_dict() ,		**A       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        _SCREAMING_SNAKE_CASE									=			self.text_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.vision_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output



