


"""simple docstring"""







import warnings

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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				int   ="xlnet"
       a  :				Optional[Any]   =["mems"]
       a  :				Union[str, Any]   ={
           "n_token": "vocab_size",  # Backward compatibility
           "hidden_size": "d_model",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=1_024		,     snake_case__=24		,     snake_case__=16		,     snake_case__=4_096		,     snake_case__="gelu"		,     snake_case__=True		,     snake_case__="bi"		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=None		,     snake_case__=True		,     snake_case__=False		,     snake_case__=False		,     snake_case__=-1		,     snake_case__=False		,     snake_case__="last"		,     snake_case__=True		,     snake_case__="tanh"		,     snake_case__=0.1		,     snake_case__=5		,     snake_case__=5		,     snake_case__=5		,     snake_case__=1		,     snake_case__=2		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							vocab_size
         lowerCAmelCase     :     int								=							d_model
         lowerCAmelCase     :     int								=							n_layer
         lowerCAmelCase     :     Union[str, Any]								=							n_head
         if d_model % n_head != 0:
           raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""						)
         if "d_head" in kwargs:
           if kwargs["d_head"] != d_model // n_head:
             raise ValueError(
                 f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""						)
         lowerCAmelCase     :     Optional[int]								=							d_model // n_head
         lowerCAmelCase     :     Optional[int]								=							ff_activation
         lowerCAmelCase     :     Dict								=							d_inner
         lowerCAmelCase     :     Union[str, Any]								=							untie_r
         lowerCAmelCase     :     Any								=							attn_type

         lowerCAmelCase     :     Optional[int]								=							initializer_range
         lowerCAmelCase     :     Optional[Any]								=							layer_norm_eps

         lowerCAmelCase     :     List[Any]								=							dropout
         lowerCAmelCase     :     Optional[Any]								=							mem_len
         lowerCAmelCase     :     Union[str, Any]								=							reuse_len
         lowerCAmelCase     :     Tuple								=							bi_data
         lowerCAmelCase     :     Optional[int]								=							clamp_len
         lowerCAmelCase     :     Dict								=							same_length

         lowerCAmelCase     :     Dict								=							summary_type
         lowerCAmelCase     :     Optional[int]								=							summary_use_proj
         lowerCAmelCase     :     Union[str, Any]								=							summary_activation
         lowerCAmelCase     :     Optional[Any]								=							summary_last_dropout
         lowerCAmelCase     :     str								=							start_n_top
         lowerCAmelCase     :     List[Any]								=							end_n_top

         lowerCAmelCase     :     List[str]								=							bos_token_id
         lowerCAmelCase     :     List[Any]								=							pad_token_id
         lowerCAmelCase     :     str								=							eos_token_id

         if "use_cache" in kwargs:
           warnings.warn(
               "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
               " instead."		,     snake_case__		,     )
           lowerCAmelCase     :     Dict								=							kwargs["use_cache"]

         lowerCAmelCase     :     str								=							use_mems_eval
         lowerCAmelCase     :     str								=							use_mems_train
         super().__init__(pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     **snake_case__						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)
         return -1







       @max_position_embeddings.setter
       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         raise NotImplementedError(
             f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)









"""simple docstring"""







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

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

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> 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(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

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

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							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()





