



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class 	__snake_case	:
  snake_case__    :       int
  snake_case__    :       TreeNode | None        =						None
  snake_case__    :       TreeNode | None        =						None


lowerCAmelCase__      =    namedtuple('''CoinsDistribResult''', '''moves excess''')






def 	snake_case_  (				A_ :		List[Any]						):


       '''simple docstring'''





       if root is None:
              return 0

       # Validation
       def count_nodes(A_ :		Optional[int]						) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left						) + count_nodes(node.right						) + 1

       def count_coins(A_ :		Any						) -> int:
              if node is None:
                     return 0

              return count_coins(node.left						) + count_coins(node.right						) + node.data

       if count_nodes(__SCREAMING_SNAKE_CASE						) != count_coins(__SCREAMING_SNAKE_CASE						):
              raise ValueError('''The nodes number should be same as the number of coins'''						)

       # Main calculation
       def get_distrib(A_ :		List[Any]						) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0,    1						)

              _lowerCamelCase						:    Optional[Any]         =			get_distrib(node.left						)
              _lowerCamelCase						:    Optional[int]         =			get_distrib(node.right						)

              _lowerCamelCase						:    Any         =			1 - left_distrib_excess
              _lowerCamelCase						:    str         =			1 - right_distrib_excess

              _lowerCamelCase						:    Dict         =			(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(__SCREAMING_SNAKE_CASE						)
                  + abs(__SCREAMING_SNAKE_CASE						)
              )
              _lowerCamelCase						:    List[Any]         =			node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE						)

       return get_distrib(__SCREAMING_SNAKE_CASE						)[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()







"""simple docstring"""






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


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[Any]        =						"xglm"
  snake_case__    :       Dict        =						["past_key_values"]

  snake_case__    :       str        =						{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=2_5_6_0_0_8						, __lowerCAmelCase					:						int=2_0_4_8						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Tuple=2_4						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Tuple="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[Any]=0.0						, __lowerCAmelCase					:						int=0.02						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						str=2						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						Dict=0						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			vocab_size
         _lowerCamelCase						:    List[Any]         =			max_position_embeddings
         _lowerCamelCase						:    int         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			ffn_dim
         _lowerCamelCase						:    Any         =			num_layers
         _lowerCamelCase						:    Union[str, Any]         =			attention_heads
         _lowerCamelCase						:    List[str]         =			activation_function
         _lowerCamelCase						:    Union[str, Any]         =			dropout
         _lowerCamelCase						:    int         =			attention_dropout
         _lowerCamelCase						:    Optional[int]         =			activation_dropout
         _lowerCamelCase						:    Any         =			layerdrop
         _lowerCamelCase						:    List[str]         =			init_std
         _lowerCamelCase						:    Union[str, Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:    str         =			use_cache

         super().__init__(
             pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )


