





'''simple docstring'''

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class __a			( _snake_case      ):
   __UpperCamelCase :			Tuple						   =						'glpn'





   def __init__(       self					:      Dict					,lowerCamelCase					:      Tuple=3					,lowerCamelCase					:      Optional[Any]=4					,lowerCamelCase					:      Dict=[2, 2, 2, 2]					,lowerCamelCase					:      Union[str, Any]=[8, 4, 2, 1]					,lowerCamelCase					:      Any=[32, 64, 160, 256]					,lowerCamelCase					:      Tuple=[7, 3, 3, 3]					,lowerCamelCase					:      List[str]=[4, 2, 2, 2]					,lowerCamelCase					:      str=[1, 2, 5, 8]					,lowerCamelCase					:      Union[str, Any]=[4, 4, 4, 4]					,lowerCamelCase					:      Any="gelu"					,lowerCamelCase					:      int=0.0					,lowerCamelCase					:      str=0.0					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      Union[str, Any]=1E-6					,lowerCamelCase					:      Dict=64					,lowerCamelCase					:      Optional[int]=10					,lowerCamelCase					:      int=-1					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							num_encoder_blocks
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							sr_ratios
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							patch_sizes
      __SCREAMING_SNAKE_CASE   				=							strides
      __SCREAMING_SNAKE_CASE   				=							mlp_ratios
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							drop_path_rate
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							decoder_hidden_size
      __SCREAMING_SNAKE_CASE   				=							max_depth
      __SCREAMING_SNAKE_CASE   				=							head_in_index












'''simple docstring'''

import os
from math import logaa

def     __magic_name__							(				__UpperCAmelCase = "base_exp.txt"   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0
   __SCREAMING_SNAKE_CASE   				=							0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )   ):
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							list(map(__UpperCAmelCase				,						line.split(""","""   )   )   )
      if x * logaa(__UpperCAmelCase   ) > largest:
         __SCREAMING_SNAKE_CASE   				=							x * logaa(__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							i + 1
   return result


if __name__ == "__main__":
    print(solution())





