





a_    :				Optional[int]					=			1000




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     return x * x




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int ,     snake_case_		:						float ,     snake_case_		:						float ,     snake_case_		:						int = 4						):
     assert callable(
         snake_case_						), f'the function(object) passed should be callable your input : {function}'
     assert isinstance(snake_case_ ,     (float, int)						), f'a should be float or integer your input : {a}'
     assert isinstance(function(snake_case_						) ,     (float, int)						), (
         "the function should return integer or float return type of your function, "
         f'{type(snake_case_						)}'
     )
     assert isinstance(snake_case_ ,     (float, int)						), f'b should be float or integer your input : {b}'
     assert (
         isinstance(snake_case_ ,     snake_case_						) and precision > 0
     ), f'precision should be positive integer your input : {precision}'

     # just applying the formula of simpson for approximate integration written in
     # mentioned article in first comment of this file and above this function

     __magic_name__				=       (b - a) / N_STEPS
     __magic_name__				=       function(snake_case_						) + function(snake_case_						)

     for i in range(1 ,     snake_case_						):
          __magic_name__				=       a + h * i
          result += function(snake_case_						) * (4 if i % 2 else 2)

     result *= h / 3
     return round(snake_case_ ,     snake_case_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


a_    :				Union[str, Any]					=			logging.get_logger(__name__)

a_    :				Optional[Any]					=			{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""vit-hybrid"""





  def __init__(  self	,  A=None	,  A=7_68	,  A=12	,  A=12	,  A=30_72	,  A="gelu"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-12	,  A=2_24	,  A=1	,  A=3	,  A=[1, 10_24, 24, 24]	,  A=True	,  **A	,  )		->   Union[str, Any]:





       '''simple docstring'''







       super().__init__(**A						)

       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
            __magic_name__				=       {
                '''global_padding''': '''same''',
                '''layer_type''': '''bottleneck''',
                '''depths''': [3, 4, 9],
                '''out_features''': ['''stage3'''],
                '''embedding_dynamic_padding''': True,
            }

       if isinstance(A	,  A						):
            if "model_type" in backbone_config:
                 __magic_name__				=       CONFIG_MAPPING[backbone_config['''model_type''']]
            else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
                 __magic_name__				=       BitConfig
            __magic_name__				=       backbone_config_class(**A						)

       __magic_name__				=       backbone_featmap_shape
       __magic_name__				=       backbone_config
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       qkv_bias







  def 			__A      (  self						)		->   Dict[str, any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(self.__dict__						)
       __magic_name__				=       self.backbone_config.to_dict()
       __magic_name__				=       self.__class__.model_type
       return output