'''simple docstring'''





import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE_:	List[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	Union[str, Any]    ={
    '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 (					UpperCamelCase__      ):
    a__			:		List[Any]			  =    """bridgetower_vision_model"""



    def __init__(self       :       Optional[int]  ,     __a       :       str=768  ,     __a       :       Dict=12  ,     __a       :       Optional[Any]=3  ,     __a       :       Dict=16  ,     __a       :       Dict=288  ,     __a       :       Union[str, Any]=1  ,     __a       :       Any=1E-05  ,     __a       :       Optional[Any]=False  ,     __a       :       Optional[Any]=True  ,     __a       :       Dict=False  ,     **__a       :       Optional[int]  ,     ):
         super().__init__(**__a					)
         UpperCAmelCase_			       =  hidden_size
         UpperCAmelCase_			       =  num_hidden_layers
         UpperCAmelCase_			       =  num_channels
         UpperCAmelCase_			       =  patch_size
         UpperCAmelCase_			       =  image_size
         UpperCAmelCase_			       =  initializer_factor
         UpperCAmelCase_			       =  layer_norm_eps
         UpperCAmelCase_			       =  stop_gradient
         UpperCAmelCase_			       =  share_layernorm
         UpperCAmelCase_			       =  remove_last_layer






    @classmethod
    def    _lowercase		(cls       :       List[str]  ,     __a       :       Union[str, os.PathLike]  ,     **__a       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  cls.get_config_dict(__a  ,     **__a					)

         if config_dict.get("model_type"					) == "bridgetower":
              UpperCAmelCase_			       =  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 (					UpperCamelCase__      ):
    a__			:		Optional[Any]			  =    """bridgetower_text_model"""



    def __init__(self       :       Tuple  ,     __a       :       List[Any]=50265  ,     __a       :       int=768  ,     __a       :       int=12  ,     __a       :       str=12  ,     __a       :       Any=1  ,     __a       :       Dict=3072  ,     __a       :       int="gelu"  ,     __a       :       Tuple=0.1  ,     __a       :       Optional[int]=0.1  ,     __a       :       Tuple=514  ,     __a       :       List[Any]=1  ,     __a       :       Tuple=1E-05  ,     __a       :       Any=1  ,     __a       :       Any=0  ,     __a       :       Dict=2  ,     __a       :       Optional[Any]="absolute"  ,     __a       :       str=True  ,     **__a       :       int  ,     ):
         super().__init__(**__a					)

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






    @classmethod
    def    _lowercase		(cls       :       Optional[int]  ,     __a       :       Union[str, os.PathLike]  ,     **__a       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  cls.get_config_dict(__a  ,     **__a					)

         if config_dict.get("model_type"					) == "bridgetower":
              UpperCAmelCase_			       =  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 (					UpperCamelCase__      ):
    a__			:		List[str]			  =    """bridgetower"""



    def __init__(self       :       int  ,     __a       :       Optional[Any]=True  ,     __a       :       Optional[Any]="gelu"  ,     __a       :       str=768  ,     __a       :       Optional[Any]=1  ,     __a       :       List[str]=1E-05  ,     __a       :       List[Any]=False  ,     __a       :       int="add"  ,     __a       :       Optional[int]=12  ,     __a       :       List[str]=6  ,     __a       :       Dict=False  ,     __a       :       Optional[int]=False  ,     __a       :       Optional[int]=None  ,     __a       :       Tuple=None  ,     **__a       :       str  ,     ):
         # TODO: remove this once the Hub files are updated.
         UpperCAmelCase_			       =  kwargs.pop("text_config_dict"  ,     __a					)
         UpperCAmelCase_			       =  kwargs.pop("vision_config_dict"  ,     __a					)

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

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

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

         UpperCAmelCase_			       =  BridgeTowerTextConfig(**__a					)
         UpperCAmelCase_			       =  BridgeTowerVisionConfig(**__a					)



    @classmethod
    def    _lowercase		(cls       :       str  ,     __a       :       BridgeTowerTextConfig  ,     __a       :       BridgeTowerVisionConfig  ,     **__a       :       Union[str, Any]					):
         return cls(text_config=text_config.to_dict()  ,     vision_config=vision_config.to_dict()  ,     **__a					)






    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  copy.deepcopy(self.__dict__					)
         UpperCAmelCase_			       =  self.text_config.to_dict()
         UpperCAmelCase_			       =  self.vision_config.to_dict()
         UpperCAmelCase_			       =  self.__class__.model_type
         return output












from ...configuration_utils import PretrainedConfig


lowerCamelCase__       		=  {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"tapas"



       def __init__(				self      ,    a__=3_05_22      ,    a__=7_68      ,    a__=12      ,    a__=12      ,    a__=30_72      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10_24      ,    a__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,    a__=0.02      ,    a__=1e-12      ,    a__=0      ,    a__=10.0      ,    a__=0      ,    a__=1.0      ,    a__=None      ,    a__=1.0      ,    a__=False      ,    a__=None      ,    a__=1.0      ,    a__=1.0      ,    a__=False      ,    a__=False      ,    a__="ratio"      ,    a__=None      ,    a__=None      ,    a__=64      ,    a__=32      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    **a__     )

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

            if isinstance(self.aggregation_labels      ,    a__     ):
                 __snake_case      :Any 							=    {int(a__     ): v for k, v in aggregation_labels.items()}


