



def       lowerCAmelCase_     (lowerCAmelCase__:						int   ):





			"""simple docstring"""


			if number < 0:
						raise ValueError("""number must not be negative"""   )
			return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()







import copy
import os
from typing import Union

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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    '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							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		'''bridgetower_vision_model'''



 def __init__(					self		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=12		,				lowerCAmelCase_=3		,				lowerCAmelCase_=16		,				lowerCAmelCase_=2_88		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				**lowerCAmelCase_		,				)  ->					Any:
        super().__init__(**lowerCAmelCase_							)
        _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      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					"PretrainedConfig":
        _A   ,  _A        =  cls.get_config_dict(lowerCAmelCase_		,				**lowerCAmelCase_							)

        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(lowerCAmelCase_		,				**lowerCAmelCase_							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		'''bridgetower_text_model'''



 def __init__(					self		,				lowerCAmelCase_=5_02_65		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=12		,				lowerCAmelCase_=12		,				lowerCAmelCase_=1		,				lowerCAmelCase_=30_72		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=5_14		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				lowerCAmelCase_="absolute"		,				lowerCAmelCase_=True		,				**lowerCAmelCase_		,				)  ->					Optional[Any]:
        super().__init__(**lowerCAmelCase_							)

        _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      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					"PretrainedConfig":
        _A   ,  _A        =  cls.get_config_dict(lowerCAmelCase_		,				**lowerCAmelCase_							)

        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(lowerCAmelCase_		,				**lowerCAmelCase_							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[int]    			=		'''bridgetower'''



 def __init__(					self		,				lowerCAmelCase_=True		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=7_68		,				lowerCAmelCase_=1		,				lowerCAmelCase_=1E-05		,				lowerCAmelCase_=False		,				lowerCAmelCase_="add"		,				lowerCAmelCase_=12		,				lowerCAmelCase_=6		,				lowerCAmelCase_=False		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					int:
        # TODO: remove this once the Hub files are updated.
        _A        =  kwargs.pop("""text_config_dict"""		,				lowerCAmelCase_							)
        _A        =  kwargs.pop("""vision_config_dict"""		,				lowerCAmelCase_							)

        super().__init__(**lowerCAmelCase_							)
        _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(**lowerCAmelCase_							)
        _A        =  BridgeTowerVisionConfig(**lowerCAmelCase_							)



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Any:
        return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, 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




