




import copy
import os
from typing import Union

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


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"bridgetower_vision_model"


    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=768       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=12       ,     SCREAMING_SNAKE_CASE__					:					Any=3       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=16       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=288       ,     SCREAMING_SNAKE_CASE__					:					List[str]=1       ,     SCREAMING_SNAKE_CASE__					:					Any=1e-0_5       ,     SCREAMING_SNAKE_CASE__					:					List[str]=False       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE__					:					int=False       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     )				->	str:
         super().__init__(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			patch_size
         lowerCAmelCase__          =			image_size
         lowerCAmelCase__          =			initializer_factor
         lowerCAmelCase__          =			layer_norm_eps
         lowerCAmelCase__          =			stop_gradient
         lowerCAmelCase__          =			share_layernorm
         lowerCAmelCase__          =			remove_last_layer


    @classmethod
    def        a				(	cls					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, os.PathLike]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	"PretrainedConfig":
         lowerCAmelCase__     ,		lowerCAmelCase__          =			cls.get_config_dict(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if config_dict.get("model_type"       ) == "bridgetower":
              lowerCAmelCase__          =			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(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"bridgetower_text_model"


    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any=50_265       ,     SCREAMING_SNAKE_CASE__					:					int=768       ,     SCREAMING_SNAKE_CASE__					:					Any=12       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=12       ,     SCREAMING_SNAKE_CASE__					:					Dict=1       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3_072       ,     SCREAMING_SNAKE_CASE__					:					int="gelu"       ,     SCREAMING_SNAKE_CASE__					:					str=0.1       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=514       ,     SCREAMING_SNAKE_CASE__					:					Tuple=1       ,     SCREAMING_SNAKE_CASE__					:					Tuple=1e-0_5       ,     SCREAMING_SNAKE_CASE__					:					Any=1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=0       ,     SCREAMING_SNAKE_CASE__					:					str=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]="absolute"       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=True       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     )				->	Any:
         super().__init__(**SCREAMING_SNAKE_CASE__       )

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


    @classmethod
    def        a				(	cls					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, os.PathLike]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	"PretrainedConfig":
         lowerCAmelCase__     ,		lowerCAmelCase__          =			cls.get_config_dict(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if config_dict.get("model_type"       ) == "bridgetower":
              lowerCAmelCase__          =			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(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"bridgetower"


    def __init__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					int=True       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=768       ,     SCREAMING_SNAKE_CASE__					:					str=1       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=1e-0_5       ,     SCREAMING_SNAKE_CASE__					:					Dict=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]="add"       ,     SCREAMING_SNAKE_CASE__					:					str=12       ,     SCREAMING_SNAKE_CASE__					:					Tuple=6       ,     SCREAMING_SNAKE_CASE__					:					Any=False       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					Any=None       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     )				->	List[Any]:
         # TODO: remove this once the Hub files are updated.
         lowerCAmelCase__          =			kwargs.pop("text_config_dict"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("vision_config_dict"       ,     SCREAMING_SNAKE_CASE__       )

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

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

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

         lowerCAmelCase__          =			BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE__       )


    @classmethod
    def        a				(	cls					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					BridgeTowerTextConfig       ,     SCREAMING_SNAKE_CASE__					:					BridgeTowerVisionConfig       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Dict:
         return cls(text_config=text_config.to_dict()       ,     vision_config=vision_config.to_dict()       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Any       )				->	Dict:
         lowerCAmelCase__          =			copy.deepcopy(self.__dict__       )
         lowerCAmelCase__          =			self.text_config.to_dict()
         lowerCAmelCase__          =			self.vision_config.to_dict()
         lowerCAmelCase__          =			self.__class__.model_type
         return output






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     if not isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			F'Input value of [number={number}] must be an integer'
          raise TypeError(lowerCAmelCase_      )
     if number < 1:
          raise ValueError("Input must be a positive integer"      )
     return -1 if len(prime_factors(lowerCAmelCase_      )      ) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()
