

"""simple docstring"""



import copy
import os
from typing import Union

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


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

SCREAMING_SNAKE_CASE_					:	Dict							  =						{
    '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"""



    UpperCAmelCase						 =					"bridgetower_vision_model"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: int=7_68    ,						UpperCamelCase: Dict=12    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: Optional[int]=16    ,						UpperCamelCase: Any=2_88    ,						UpperCamelCase: Union[str, Any]=1    ,						UpperCamelCase: List[Any]=1e-0_5    ,						UpperCamelCase: List[Any]=False    ,						UpperCamelCase: str=True    ,						UpperCamelCase: List[str]=False    ,						**UpperCamelCase: Union[str, Any]    ,						):






         """simple docstring"""


         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 				UpperCamelCase	(     cls: List[Any]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"bridgetower_text_model"

    def __init__(     self: int    ,						UpperCamelCase: Optional[Any]=5_02_65    ,						UpperCamelCase: Tuple=7_68    ,						UpperCamelCase: Any=12    ,						UpperCamelCase: Optional[int]=12    ,						UpperCamelCase: Any=1    ,						UpperCamelCase: List[Any]=30_72    ,						UpperCamelCase: Tuple="gelu"    ,						UpperCamelCase: str=0.1    ,						UpperCamelCase: Optional[Any]=0.1    ,						UpperCamelCase: Dict=5_14    ,						UpperCamelCase: int=1    ,						UpperCamelCase: Dict=1e-0_5    ,						UpperCamelCase: Optional[Any]=1    ,						UpperCamelCase: Union[str, Any]=0    ,						UpperCamelCase: Any=2    ,						UpperCamelCase: Dict="absolute"    ,						UpperCamelCase: Dict=True    ,						**UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


         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 				UpperCamelCase	(     cls: int    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"bridgetower"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=True    ,						UpperCamelCase: List[str]="gelu"    ,						UpperCamelCase: List[str]=7_68    ,						UpperCamelCase: Optional[Any]=1    ,						UpperCamelCase: List[str]=1e-0_5    ,						UpperCamelCase: Tuple=False    ,						UpperCamelCase: str="add"    ,						UpperCamelCase: str=12    ,						UpperCamelCase: List[Any]=6    ,						UpperCamelCase: int=False    ,						UpperCamelCase: int=False    ,						UpperCamelCase: Optional[Any]=None    ,						UpperCamelCase: List[str]=None    ,						**UpperCamelCase: Optional[int]    ,						):






         """simple docstring"""


         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 				UpperCamelCase	(     cls: Dict    ,						UpperCamelCase: BridgeTowerTextConfig    ,						UpperCamelCase: BridgeTowerVisionConfig    ,						**UpperCamelCase: str       ):






         """simple docstring"""


         return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**UpperCamelCase       )





    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         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"""



import math
import sys





def  _snake_case     (    UpperCAmelCase_  :					int			):
     if number != int(UpperCAmelCase_			):
          raise ValueError("""the value of input must be a natural number"""			)
     if number < 0:
          raise ValueError("""the value of input must not be a negative number"""			)
     if number == 0:
          return 1
     A__										=  [-1] * (number + 1)
     A__										=  0
     for i in range(1					,   number + 1			):
          A__										=  sys.maxsize
          A__										=  int(math.sqrt(UpperCAmelCase_			)			)
          for j in range(1					,   root + 1			):
               A__										=  1 + answers[i - (j**2)]
               A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          A__										=  answer
     return answers[number]


if __name__ == "__main__":
 import doctest

 doctest.testmod()






