
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__          =logging.get_logger(__name__)

lowercase__          ={
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				"data2vec-vision"
   def __init__(self			: Optional[Any] ,    snake_case_			: List[Any]=7_6_8 ,    snake_case_			: Tuple=1_2 ,    snake_case_			: Any=1_2 ,    snake_case_			: Optional[int]=3_0_7_2 ,    snake_case_			: Tuple="gelu" ,    snake_case_			: Dict=0.0 ,    snake_case_			: Tuple=0.0 ,    snake_case_			: Dict=0.02 ,    snake_case_			: Optional[int]=1E-12 ,    snake_case_			: int=2_2_4 ,    snake_case_			: Optional[Any]=1_6 ,    snake_case_			: List[str]=3 ,    snake_case_			: Tuple=False ,    snake_case_			: Any=False ,    snake_case_			: Optional[int]=False ,    snake_case_			: str=False ,    snake_case_			: Any=0.1 ,    snake_case_			: int=0.1 ,    snake_case_			: Optional[Any]=True ,    snake_case_			: Union[str, Any]=[3, 5, 7, 1_1] ,    snake_case_			: Any=[1, 2, 3, 6] ,    snake_case_			: int=True ,    snake_case_			: Optional[Any]=0.4 ,    snake_case_			: Tuple=2_5_6 ,    snake_case_			: Optional[Any]=1 ,    snake_case_			: List[Any]=False ,    snake_case_			: Union[str, Any]=2_5_5 ,    **snake_case_			: int ,    ):
    super().__init__(**snake_case_       )

    __a			:  List[str]		    =  hidden_size
    __a			:  List[str]		    =  num_hidden_layers
    __a			:  Any		    =  num_attention_heads
    __a			:  List[str]		    =  intermediate_size
    __a			:  Optional[int]		    =  hidden_act
    __a			:  int		    =  hidden_dropout_prob
    __a			:  List[str]		    =  attention_probs_dropout_prob
    __a			:  Any		    =  initializer_range
    __a			:  str		    =  layer_norm_eps

    __a			:  List[str]		    =  image_size
    __a			:  Optional[Any]		    =  patch_size
    __a			:  int		    =  num_channels
    __a			:  Tuple		    =  use_mask_token
    __a			:  List[Any]		    =  use_absolute_position_embeddings
    __a			:  Tuple		    =  use_relative_position_bias
    __a			:  Any		    =  use_shared_relative_position_bias
    __a			:  Dict		    =  layer_scale_init_value
    __a			:  int		    =  drop_path_rate
    __a			:  Optional[int]		    =  use_mean_pooling
    # decode head attributes (semantic segmentation)
    __a			:  Union[str, Any]		    =  out_indices
    __a			:  Union[str, Any]		    =  pool_scales
    # auxiliary head attributes (semantic segmentation)
    __a			:  List[Any]		    =  use_auxiliary_head
    __a			:  List[Any]		    =  auxiliary_loss_weight
    __a			:  str		    =  auxiliary_channels
    __a			:  int		    =  auxiliary_num_convs
    __a			:  Union[str, Any]		    =  auxiliary_concat_input
    __a			:  Dict		    =  semantic_loss_ignore_index





class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				version.parse("1.11"   )
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return OrderedDict(
        [
            ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
        ]       )







   @property
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    return 1E-4


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


if __name__ == "__main__":
      print(F"""{solution() = }""")
