





"""simple docstring"""
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


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  '''beit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=81_92		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11]		,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6]		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0.4		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2_55		,**SCREAMING_SNAKE_CASE__		,)      ->				Any:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Tuple		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :str		    =  layer_norm_eps

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






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Any     						=  version.parse('''1.11'''  )




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             return OrderedDict(
                 [
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ]							)




       @property
       def    _UpperCamelCase					(						self							)      ->				float:





             """simple docstring"""






             return 1E-4






"""simple docstring"""
from __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

      if depth < 0:
            raise ValueError('''Depth cannot be less than 0'''							)

      if len(a_							) == 0:
            raise ValueError('''Scores cannot be empty'''							)

      if depth == height:
            return scores[node_index]

      if is_max:
            return max(
                minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

      return min(
          minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()